{"id":4362,"date":"2020-01-28T13:15:14","date_gmt":"2020-01-28T13:15:14","guid":{"rendered":"https:\/\/fei-lab.org\/?p=4362"},"modified":"2023-12-28T20:01:08","modified_gmt":"2023-12-28T20:01:08","slug":"artificial-intelligence","status":"publish","type":"post","link":"https:\/\/fei-lab.org\/artificial-intelligence\/","title":{"rendered":"Artificial Intelligence"},"content":{"rendered":"\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-av_heading-6e7d5f2c07673afff3a3310a505789a7\">\n#top .av-special-heading.av-av_heading-6e7d5f2c07673afff3a3310a505789a7{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-av_heading-6e7d5f2c07673afff3a3310a505789a7 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-av_heading-6e7d5f2c07673afff3a3310a505789a7 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-av_heading-6e7d5f2c07673afff3a3310a505789a7 av-special-heading-h3  avia-builder-el-0  el_before_av_image  avia-builder-el-first '><h3 class='av-special-heading-tag '  itemprop=\"headline\"  >Artificial Intelligence and Machine Learning<\/h3><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-k5xwx6hm-7e546b21af4d8e0d98461568e0e705ee\">\n.avia-image-container.av-k5xwx6hm-7e546b21af4d8e0d98461568e0e705ee img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-k5xwx6hm-7e546b21af4d8e0d98461568e0e705ee .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-k5xwx6hm-7e546b21af4d8e0d98461568e0e705ee av-styling- av-img-linked avia-align-center  avia-builder-el-1  el_after_av_heading  el_before_av_heading '   itemprop=\"image\" itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/ImageObject\" ><div class=\"avia-image-container-inner\"><div class=\"avia-image-overlay-wrap\"><a href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/01\/FI-artificial-intelligence-1030x389.png\" data-srcset=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/01\/FI-artificial-intelligence-1030x389.png 1030w, https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/01\/FI-artificial-intelligence-300x113.png 300w, https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/01\/FI-artificial-intelligence-768x290.png 768w, https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/01\/FI-artificial-intelligence-1536x580.png 1536w, https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/01\/FI-artificial-intelligence-1500x566.png 1500w, https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/01\/FI-artificial-intelligence-705x266.png 705w, https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/01\/FI-artificial-intelligence.png 1600w\" data-sizes=\"(max-width: 1030px) 100vw, 1030px\" class='avia_image '  aria-label='FI artificial intelligence'><img decoding=\"async\" fetchpriority=\"high\" class='wp-image-5145 avia-img-lazy-loading-not-5145 avia_image ' src=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/01\/FI-artificial-intelligence.png\" alt='artificial intelligence' title='FI artificial intelligence'  height=\"604\" width=\"1600\"  itemprop=\"thumbnailUrl\" srcset=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/01\/FI-artificial-intelligence.png 1600w, https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/01\/FI-artificial-intelligence-300x113.png 300w, https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/01\/FI-artificial-intelligence-1030x389.png 1030w, https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/01\/FI-artificial-intelligence-768x290.png 768w, https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/01\/FI-artificial-intelligence-1536x580.png 1536w, https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/01\/FI-artificial-intelligence-1500x566.png 1500w, https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/01\/FI-artificial-intelligence-705x266.png 705w\" sizes=\"(max-width: 1600px) 100vw, 1600px\" \/><\/a><\/div><\/div><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-av_heading-e6cf3d52a49c8be0eb5535ffe7efaae8\">\n#top .av-special-heading.av-av_heading-e6cf3d52a49c8be0eb5535ffe7efaae8{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-av_heading-e6cf3d52a49c8be0eb5535ffe7efaae8 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-av_heading-e6cf3d52a49c8be0eb5535ffe7efaae8 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-av_heading-e6cf3d52a49c8be0eb5535ffe7efaae8 av-special-heading-h3  avia-builder-el-2  el_after_av_image  el_before_av_textblock '><h3 class='av-special-heading-tag '  itemprop=\"headline\"  >Selected Publications<\/h3><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div>\n<section  class='av_textblock_section av-k5xwv6cv-6345046671da49fc89d26dd6e6463c20 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" >\r\n<style>\r\n\tp {display: inline;}\r\n<\/style>\r\n<ul>\r\n\r\n<li>\r\n\r\n<p>Ma L, Rathgeb A, Mubarak H, Tran M, <strong>Fei B<\/strong>. Unsupervised super-resolution reconstruction of hyperspectral histology images for whole-slide imaging. Journal of biomedical optics. 2022 May 1;27(5):056502-.<\/p>\n \r\n\r\n\r\n\r\n\r\nMa_2022_JBO_SuperRes_Reconstruction_WSI\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/35578386\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2023\/02\/Ma_2022_JBO_SuperRes_Reconstruction_WSI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/1.JBO.27.5.059801\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2023\/02\/Ma_2022_JBO_SuperRes_Reconstruction_WSI_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2023\/02\/Ma_2022_JBO_SuperRes_Reconstruction_WSI_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Zhou X, Ma L, Mubarak HK, Little JV, Chen AY, Myers LL, Sumer BD, <strong>Fei B<\/strong>. Automatic detection of head and neck squamous cell carcinoma on pathologic slides using polarized hyperspectral imaging and deep learning. In Medical Imaging 2022: Digital and Computational Pathology 2022 Apr 4 (Vol. 12039, pp. 91-100). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_SPIE_Ximing_PHSI_Histology\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/34955584\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Ximing_PHSI_Histology.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2614624\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Ximing_PHSI_Histology_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Ximing_PHSI_Histology_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Chaudhary U, Leitch K, Chhabra A, Kohli A, <strong>Fei B<\/strong>. Deep learning-based abdominal muscle segmentation on CT images of surgical patient populations. In Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging 2022 Apr 4 (Vol. 12036, pp. 453-459). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_SPIE_Usamah_CT_Segmentation\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/36845411\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Usamah_CT_Segmentation.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2611773\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Usamah_CT_Segmentation_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Usamah_CT_Segmentation_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Pathour T, Akter N, Dormer JD, Chaudhary S, <strong>Fei B<\/strong>, Sirsi S. Identifying unique acoustic signatures from chemically-crosslinked microbubble clusters using deep learning. In Medical Imaging 2022: Ultrasonic Imaging and Tomography 2022 Apr 4 (Vol. 12038, pp. 128-136). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_SPIE_Teja_Ultrasound_DL\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/36793945\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Teja_Ultrasound_DL.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2611572\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Teja_Ultrasound_DL_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Teja_Ultrasound_DL_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Shi T, Shahedi M, Caughlin K, Dormer JD, Ma L, <strong>Fei B<\/strong>. Semi-automated three-dimensional segmentation for cardiac CT images using deep learning and randomly distributed points. In Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling 2022 Apr 4 (Vol. 12034, pp. 424-430). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_SPIE_Ted_Cardiac_Segmentation\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/36793655\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Ted_Cardiac_Segmentation.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2611594\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Ted_Cardiac_Segmentation_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Ted_Cardiac_Segmentation_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Tran MH, Ma L, Litter JV, Chen AY, <strong>Fei B<\/strong>. Thyroid carcinoma detection on whole histologic slides using hyperspectral imaging and deep learning. In Medical Imaging 2022: Digital and Computational Pathology 2022 Apr 4 (Vol. 12039, pp. 101-111). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_SPIE_Minh_HSI_Histology\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/36798939\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Minh_HSI_Histology.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2612963\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Minh_HSI_Histology_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Minh_HSI_Histology_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Shahedi M, Dormer JD, Do QN, Xi Y, Lewis MA, Herrera CL, Spong CY, Madhuranthakam AJ, Twickler DM, <strong>Fei B<\/strong>. Automatic segmentation of uterine cavity and placenta on MR Images using deep learning. In Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging 2022 Apr 4 (Vol. 12036, pp. 287-293). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_SPIE_Maysam_Placenta_Segmentation\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/36798450\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Maysam_Placenta_Segmentation.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2613286\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Maysam_Placenta_Segmentation_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Maysam_Placenta_Segmentation_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Ma L, Rathgeb A, Tran M, <strong>Fei B<\/strong>. Unsupervised super resolution network for hyperspectral histologic imaging. In Medical Imaging 2022: Digital and Computational Pathology 2022 Apr 4 (Vol. 12039, pp. 149-159). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_SPIE_Ling_HSI_Super_Resolution\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/36793770\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Ling_HSI_Super_Resolution.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2611889\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Ling_HSI_Super_Resolution_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Ling_HSI_Super_Resolution_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Leitch K, Shahedi M, Dormer JD, Do QN, Xi Y, Lewis MA, Herrera CL, Spong CY, Madhuranthakam AJ, Twickler DM, <strong>Fei B<\/strong>. Placenta accreta spectrum and hysterectomy prediction using MRI radiomic features. In Medical Imaging 2022: Computer-Aided Diagnosis 2022 Apr 4 (Vol. 12033, pp. 394-399). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_SPIE_KaToria_Placenta_Radiomics\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/36844110\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_KaToria_Placenta_Radiomics.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2611587\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_KaToria_Placenta_Radiomics_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_KaToria_Placenta_Radiomics_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Leitch K, Halicek M, Shahedi M, Little JV, Chen AY, <strong>Fei B<\/strong>. Detecting aggressive papillary thyroid carcinoma using hyperspectral imaging and radiomic features. In Medical Imaging 2022: Computer-Aided Diagnosis 2022 Apr 4 (Vol. 12033, pp. 537-544). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_SPIE_KaToria_HSI_Thyroid_Radiomics\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30220773\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_KaToria_HSI_Thyroid_Radiomics.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2611842\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_KaToria_HSI_Thyroid_Radiomics_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_KaToria_HSI_Thyroid_Radiomics_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Huang J, Guo J, Pedrosa I, <strong>Fei B<\/strong>. Deep learning-based deformable registration of dynamic contrast enhanced MR images of the kidney. In Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling 2022 Apr 4 (Vol. 12034, pp. 213-222). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_SPIE_Huang_Kidney_Segmentation\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/36793654\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Huang_Kidney_Segmentation.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2611768\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Huang_Kidney_Segmentation_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Huang_Kidney_Segmentation_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Dormer JD, Villordon M, Shahedi M, Do QN, Xi Y, Lewis MA, Madhuranthakam AJ, Herrera CL, Spong CY, Twickler DM, <strong>Fei B<\/strong>. CascadeNet for hysterectomy prediction in pregnant women due to placenta accreta spectrum. In Medical Imaging 2022: Image Processing 2022 Apr 4 (Vol. 12032, pp. 156-164). SPIE.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_SPIE_Dormer_Placenta_Cascade_Net\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/25426271\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Dormer_Placenta_Cascade_Net.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2611580\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Dormer_Placenta_Cascade_Net_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_SPIE_Dormer_Placenta_Cascade_Net_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Ma L, Little JV, Chen AY, Myers L, Sumer BD, <strong>Fei B<\/strong>. Automatic detection of head and neck squamous cell carcinoma on histologic slides using hyperspectral microscopic imaging. Journal of Biomedical Optics. 2022 Apr;27(4):046501.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_Ling_JBO_HSI_Histology\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/35484692\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_Ling_JBO_HSI_Histology.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/1.JBO.27.5.059802\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/04\/Fei_2022_Ling_JBO_HSI_Histology_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/04\/Fei_2022_Ling_JBO_HSI_Histology_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Mehta R, Filos A, Baid U, Sako C, McKinley R, Rebsamen M, D\u00e4twyler K, Meier R, Radojewski P, Murugesan GK, Nalawade S, <strong>Fei B<\/strong>. QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation-Analysis of Ranking Scores and Benchmarking Results. Journal of Machine Learning for Biomedical Imaging. 2022;1.<\/p>\n \r\n\r\n\r\n\r\n\r\nMehta_2022_MICCAI\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/36998700\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2023\/02\/Mehta_2022_MICCAI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2023\/02\/Mehta_2022_MICCAI_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2023\/02\/Mehta_2022_MICCAI_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Shahedi M, Dormer JD, Halicek M, <strong>Fei B<\/strong>. The effect of image annotation with minimal manual interaction for semiautomatic prostate segmentation in CT images using fully convolutional neural networks. Medical physics. 2022 Feb;49(2):1153-60.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2021_MP_Maysam_CT_Segmentation\r\n\r\n\t\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_MP_Maysam_CT_Segmentation.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_MP_Maysam_CT_Segmentation_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_MP_Maysam_CT_Segmentation_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Yogananda CGB, Shah BR, Yu FF, Nalawade SS, Holcomb J, Reddy D, Wagner BC, Pinho MC, Mickey B, Patel TR, <strong>Fei B<\/strong>, Madhuranthakam AJ, Maldjian JA. 5 &#8211; Simultaneous brain tumor segmentation and molecular profiling using deep learning and T2w magnetic resonance images, Brain Tumor MRI Image Segmentation Using Deep Learning Techniques,<br \/>\nAcademic Press, 2022, Pages 57-79.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_BrainTumorMRI_Yogananda\r\n\r\n\t\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_BrainTumorMRI_Yogananda.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_BrainTumorMRI_Yogananda_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Nalawade SS, Fang FY, Yogananda CG, Murugesan GK, Shah BR, Pinho MC, Wagner BC, Xi Y, Mickey B, Patel TR,<strong> Fei B<\/strong>. Brain tumor IDH, 1p\/19q, and MGMT molecular classification using MRI-based deep learning: an initial study on the effect of motion and motion correction. Journal of Medical Imaging. 2022 Jan;9(1):016001.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2022_JMI_Brain_MRI\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/24236230\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_JMI_Brain_MRI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/1.JMI.9.1.016001\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_JMI_Brain_MRI_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2022_JMI_Brain_MRI_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Shahedi M, Spong CY, Dormer JD, Do QN, Xi Y, Lewis MA, Herrera C, Madhuranthakam AJ, Twickler DM, <strong>Fei B<\/strong>. Deep learning-based segmentation of the placenta and uterus on MR images. Journal of Medical Imaging. 2021 Sep;8(5):054001.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2021_JMI_Placenta_DL\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/34589556\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2021\/09\/Fei_2021_JMI_Placenta_DL.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/1.JMI.8.5.054001\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2021\/09\/Fei_2021_JMI_Placenta_DL_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2021\/09\/Fei_2021_JMI_Placenta_DL_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Shabestri B, Anastasio MA, <strong>Fei B<\/strong>, Leblond F. Special Series Guest Editorial: Artificial Intelligence and Machine Learning in Biomedical Optics. Journal of biomedical optics. 2021 May;26(5).<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2021_JBO_Editorial_AI\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/33973425\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2021\/05\/Fei_2021_JBO_Editorial_AI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/1.JBO.26.5.052901\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2021\/05\/Fei_2021_JBO_Editorial_AI_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2021\/05\/Fei_2021_JBO_Editorial_AI_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Yogananda CG, Shah BR, Nalawade SS, Murugesan GK, Yu FF, Pinho MC, Wagner BC, Mickey B, Patel TR,<strong> Fei B<\/strong>, Madhuranthakam AJ. MRI-based deep-learning method for determining glioma MGMT promoter methylation status. American Journal of Neuroradiology. 2021 May 1;42(5):845-52.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2021_GLIOMA\r\n\r\n\t\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_GLIOMA.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_GLIOMA_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_GLIOMA_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Zhou X, Ma L, Brown W, Little JV, Chen AY, Myers LL, Sumer BD, <strong>Fei B<\/strong>. Automatic detection of head and neck squamous cell carcinoma on pathologic slides using polarized hyperspectral imaging and machine learning. In Medical Imaging 2021: Digital Pathology 2021 Feb 15 (Vol. 11603, p. 116030Q). International Society for Optics and Photonics.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2021_Zhou_HSI_HNC\r\n\r\n\t\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Zhou_HSI_HNC.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Zhou_HSI_HNC_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Zhou_HSI_HNC_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Xi Y, Shahedi M, Do QN, Dormer J, Lewis MA, <strong>Fei B<\/strong>, Spong CY, Madhuranthakam AJ, Twickler DM. Assessing reproducibility in magnetic resonance (MR) radiomics features between deep-learning segmented and expert manual segmented data and evaluating their diagnostic performance in pregnant women with suspected placenta accreta spectrum (PAS). In Medical Imaging 2021: Computer-Aided Diagnosis 2021 Feb 15 (Vol. 11597, p. 115972P). International Society for Optics and Photonics.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2021_Xi_Placenta_MRI\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/35784397\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Xi_Placenta_MRI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2581467\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Xi_Placenta_MRI_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Xi_Placenta_MRI_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Tao L, Ma L, Xie M, Liu X, Tian Z, <strong>Fei B<\/strong>. Automatic segmentation of the prostate on MR images based on anatomy and deep learning. In Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling 2021 Feb 15 (Vol. 11598, p. 115981N). International Society for Optics and Photonics.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2021_Prostate_MRI\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/35755404\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Prostate_MRI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2581893\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Prostate_MRI_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Prostate_MRI_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Ma L, Shahedi M, Shi T, Halicek M, Little JV, Chen AY, Myers LL, Sumer BD, <strong>Fei B<\/strong>. Pixel-level tumor margin assessment of surgical specimen in hyperspectral imaging and deep learning classification. In Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling 2021 Feb 15 (Vol. 11598, p. 1159811). International Society for Optics and Photonics.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2021_Ma_Margin_HSI_Deep_Learning\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/35755403\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Ma_Margin_HSI_Deep_Learning.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2581046\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Ma_Margin_HSI_Deep_Learning_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Ma_Margin_HSI_Deep_Learning_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Caughlin K, Shahedi M, Shoag JE, Barbieri C, Margolis D, <strong>Fei B<\/strong>. Three-dimensional prostate CT segmentation through fine-tuning of a pre-trained neural network using no reference labeling. InMedical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling 2021 Feb 15 (Vol. 11598, p. 115980L). International Society for Optics and Photonics.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2021_Caughlin_Prostate_CT_Segmentation\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/35755405\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Caughlin_Prostate_CT_Segmentation.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2581963\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Caughlin_Prostate_CT_Segmentation_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2021_Caughlin_Prostate_CT_Segmentation_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<div class=\"gs_citr\" tabindex=\"0\">Yogananda CG, Shah BR, Yu FF, Pinho MC, Nalawade SS, Murugesan GK, Wagner BC, Mickey B, Patel TR, <strong>Fei BW<\/strong>, Madhuranthakam AJ. A novel fully automated MRI-based deep-learning method for classification of 1p\/19q co-deletion status in brain gliomas. Neuro-oncology advances. 2020 Jan; 2(1):vdaa066.<\/div>\n \r\n\r\n\r\n\r\n\r\nYogananda_2020_NeuroOnc_1\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32705083\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/09\/Fei_2020_NeuroOnc_Yogananda_MRI_Deep_learning_Codeletion.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1093\/noajnl\/vdaa066\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/09\/Yogananda_2020_NeuroOnc_1_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/09\/Yogananda_2020_NeuroOnc_1_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<div class=\"gs_citr\" tabindex=\"0\">Yogananda CG, Shah BR, Vejdani-Jahromi M, Nalawade SS, Murugesan GK, Yu FF, Pinho MC, Wagner BC, Emblem KE, Bj\u00f8rnerud A, <strong>Fei BW<\/strong> (Corresponding author). A fully automated deep learning network for brain tumor segmentation. Tomography. 2020 Jun; 6(2):186.<\/div>\n \r\n\r\n\r\n\r\n\r\nYogananda_2020_Tomography\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32548295\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/09\/Fei_2020_Yogananda_Tomography_6_2.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.18383\/j.tom.2019.00026\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/09\/Yogananda_2020_Tomography_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/09\/Yogananda_2020_Tomography_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Tian Z, Li X, Zheng Y, Chen Z, Shi Z, Liu L, <strong>Fei B<\/strong>. Graph\u2010convolutional\u2010network\u2010based interactive prostate segmentation in MR images. Medical physics. 2020 Sep;47(9):4164-76.<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2020_Tian_MP_Prostate_Segmentation\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32533855\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2020_Tian_MP_Prostate_Segmentation.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1002\/mp.14327\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2020_Tian_MP_Prostate_Segmentation_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2022\/05\/Fei_2020_Tian_MP_Prostate_Segmentation_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<div class=\"gs_citr\" tabindex=\"0\">Ortega S, Halicek M, Fabelo H, Camacho R, Plaza MD, Godtliebsen F, M Callic\u00f3 G, <strong>Fei BW <\/strong>(Corresponding author). Hyperspectral imaging for the detection of glioblastoma tumor cells in H&amp;E slides using convolutional neural networks. Sensors; 20(7):1911.<\/div>\n \r\n\r\n\r\n\r\n\r\nOrtega_2020_Sensors\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32235483\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_Samuel_Sensors_GBM_Cells_HSI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.3390\/s20071911\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Ortega_2020_Sensors_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Ortega_2020_Sensors_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Yogananda CG, Shah BR, Nalawade S, Murugesan GK, Frank FY, Pinho MC, Wagner BC, Mickey B, Patel TR, <strong>Fei BW<\/strong>, Madhuranthakam AJ, Maldjian JA. MRI-based deep learning method for determining methylation status of the 06 methylguanne DNA methyltransferase promoter outperforms tissue based methods in brain gliomas. bioRxiv 2020.<\/p>\n \r\n\r\n\r\n\r\n\r\nYogananda_2020_bioRxiv\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/37760146\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_Maldjian_MRI_DL_Glioma.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Yogananda_2020_bioRxiv_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Yogananda_2020_bioRxiv_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<div class=\"gs_citr\" tabindex=\"0\">Nalawade S, Fang FY, Yogananda CG, Murugesan GK, Shah BR, Pinho MC, Wagner BC, Mickey B, Patel TR, <strong>Fei BW<\/strong>, Madhuranthakam AJ, Maldjian JA. Brain tumor IDH, 1p\/19q, and MGMT molecular classification using MRI-based deep learning: effect of motion and motion correction. bioRxiv 2020.<\/div>\n \r\n\r\n\r\n\r\n\r\nNalawade_2020_bioRxiv\r\n\r\n\t\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_bioRxiv_Maldjian_MRI_IDH.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Nalawade_2020_bioRxiv_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Nalawade_2020_bioRxiv_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Tran CT, Halicek M, Dormer JD, Tandon A, Hussain T, <strong>Fei BW<\/strong> (Corresponding author). Fully automated segmentation of the right ventricle in patients with repaired Tetralogy of Fallot using U-Net. Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 11317(113171M). International Society for Optics and Photonics,<\/p>\n \r\n\r\n\r\n\r\n\r\nTran_2020_SPIE\r\n\r\n\t\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_Heart_Segmentation_113171M.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2549052\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Tran_2020_SPIE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Tran_2020_SPIE_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<div class=\"gs_citr\" tabindex=\"0\">Ma L, Halicek M, <strong>Fei BW<\/strong>. In vivo cancer detection in animal model using hyperspectral image classification with wavelet feature extraction. Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 11317(113171C). International Society for Optics and Photonics,<\/div>\n \r\n\r\n\r\n\r\n\r\nMa_2020_SPIE_2\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32476705\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_LingMa_HSI_113171C.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2549397\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Ma_2020_SPIE_2_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Ma_2020_SPIE_2_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<div class=\"gs_citr\" tabindex=\"0\">Edwards K, Chhabra A, Dormer J, Jones P, Boutin RD, Lenchik L, <strong>Fei BW <\/strong>(Corresponding author). Abdominal muscle segmentation from CT using a convolutional neural network. Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 11317(113170L). International Society for Optics and Photonics,<\/div>\n \r\n\r\n\r\n\r\n\r\nEdwards_2020_SPIE\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32577045\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_KaToria_Segmentation_113170L.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2549406\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Edwards_2020_SPIE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Edwards_2020_SPIE_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<div class=\"gs_citr\" tabindex=\"0\">Halicek M, Dormer JD, Little JV, Chen AY, <strong>Fei BW <\/strong>(Corresponding author). Tumor detection of the thyroid and salivary glands using hyperspectral imaging and deep learning. Biomedical Optics Express; 11(3):1383-400.<\/div>\n \r\n\r\n\r\n\r\n\r\nHalicek_2020_BOE_2\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32206417\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_BOE_Halicek_Tumor_detection_HSI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1364\/BOE.381257\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Halicek_2020_BOE_2_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Halicek_2020_BOE_2_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Bangalore Yogananda CG, Shah BR, Vejdani-Jahromi M, Nalawade SS, Murugesan GK, Yu FF, Pinho MC, Wagner BC, Mickey B, Patel TR, <strong>Fei BW <\/strong>(Corresponding author). A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas. Neuro-oncology; 22(3):402-11.<\/p>\n \r\n\r\n\r\n\r\n\r\nYogananda_2020_NeuroOnc\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31637430\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_Neuro-Oncology_MRI_DL_IDH.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1093\/neuonc\/noz199\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Yogananda_2020_NeuroOnc_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Yogananda_2020_NeuroOnc_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Shahedi M, Halicek M, Dormer JD, <strong>Fei BW <\/strong>(Corresponding author). Incorporating minimal user input into deep learning based image segmentation. Medical Imaging 2020: Image Processing; 11313(1131313). International Society for Optics and Photonics,<\/p>\n \r\n\r\n\r\n\r\n\r\nShahedi_2020_SPIE_2\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32476701\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_Maysam_Deep_Learning_1131313.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2549716\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Shahedi_2020_SPIE_2_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Shahedi_2020_SPIE_2_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Mavuduru A, Halicek M, Shahedi M, Little JV, Chen AY, Myers LL, <strong>Fei BW<\/strong> (Corresponding author). Using a 22-layer U-Net to perform segmentation of squamous cell carcinoma on digitized head and neck histological images. Medical Imaging 2020: Digital Pathology; 11320(113200C). International Society for Optics and Photonics,<\/p>\n \r\n\r\n\r\n\r\n\r\nMavuduru_2020_SPIE\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32476709\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_Amol_Histology_113200C.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2549061\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Mavuduru_2020_SPIE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Mavuduru_2020_SPIE_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Shahedi M, Dormer JD, TT AD, Do QN, Xi Y, Lewis MA, Madhuranthakam AJ, Twickler DM, <strong>Fei BW <\/strong>(Corresponding author). Segmentation of uterus and placenta in MR images using a fully convolutional neural network. Medical Imaging 2020: Computer-Aided Diagnosis; 11314(113141R). International Society for Optics and Photonics,<\/p>\n \r\n\r\n\r\n\r\n\r\nShahedi_2020_SPIE\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32476702\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_Maysam_Placenta_113141R.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2549873\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Shahedi_2020_SPIE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Shahedi_2020_SPIE_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Mahajan A, Dormer J, Li Q, Chen D, Zhang Z, <strong>Fei BW<\/strong> (Corresponding author). Siamese neural networks for the classification of high-dimensional radiomic features. Medical Imaging 2020: Computer-Aided Diagnosis; 11314(113143Q). International Society for Optics and Photonics,<\/p>\n \r\n\r\n\r\n\r\n\r\nMahajan_2020_SPIE\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32528215\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_SPIE_Radiomic_Feature_113143Q.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2549389\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Mahajan_2020_SPIE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Mahajan_2020_SPIE_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Ortega S, Halicek M, Fabelo H, Guerra R, Lopez C, Lejeune M, Godtliebsen F, Callico GM, <strong>Fei BW <\/strong>(Corresponding author). Hyperspectral imaging and deep learning for the detection of breast cancer cells in digitized histological images. Medical Imaging 2020: Digital Pathology; 11320(113200V). International Society for Optics and Photonics,<\/p>\n \r\n\r\n\r\n\r\n\r\nOrtega_2020_SPIE\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32528219\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_Orgega_HSI_113200V.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2548609\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Ortega_2020_SPIE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Ortega_2020_SPIE_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Halicek M, Ortega S, Fabelo H, Lopez C, Lejeune M, Callico GM, <strong>Fei BW <\/strong>(Corresponding author). Conditional generative adversarial network for synthesizing hyperspectral images of breast cancer cells from digitized histology. Medical Imaging 2020: Digital Pathology; 11320(113200U). International Society for Optics and Photonics,<\/p>\n \r\n\r\n\r\n\r\n\r\nHalicek_2020_SPIE\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32528218\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_Martin_HSI_113200U.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2549994\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Halicek_2020_SPIE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Halicek_2020_SPIE_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Ma L, Halicek M, Zhou X, Dormer J, <strong>Fei BW <\/strong>(Corresponding author). Hyperspectral microscopic imaging for automatic detection of head and neck squamous cell carcinoma using histologic image and machine learning. Medical Imaging 2020: Digital Pathology; 11320(113200W). International Society for Optics and Photonics,<\/p>\n \r\n\r\n\r\n\r\n\r\nMa_2020_SPIE\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32476708\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_MaLing_HSI_113200W.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2549369\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Ma_2020_SPIE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Ma_2020_SPIE_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Selim M, Zhang J, <strong>Fei BW<\/strong>, Zhang GQ, Chen J. STAN-CT: Standardizing CT Image using Generative Adversarial Network. arXiv preprint; arXiv: 2004.01307.<\/p>\n \r\n\r\n\r\n\r\n\r\nSelim_2020_arXiv\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/33936486\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_arXiv_Selim_STAN_CT.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Selim_2020_arXiv_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Selim_2020_arXiv_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Hao D, Ding S, Qiu L, Lv Y,<strong> Fei BW<\/strong>, Zhu Y, Qin B. Sequential vessel segmentation via deep channel attention network. Neural Networks.<\/p>\n \r\n\r\n\r\n\r\n\r\nHao_2020_NeuralN\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/16039535\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Fei_2020_NeuralN_Hao_Sequential_vessel_deep_channel.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1016\/j.neunet.2020.05.005\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Hao_2020_NeuralN_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/06\/Hao_2020_NeuralN_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Twickler DM, Do QN, Yin Xi, Shahedi M, Dormer J, Devi TT A, Lewis MA, Spong CY, Dashe JS, Madhuranthakam A,\u00a0<strong>Fei BW\u00a0<\/strong>(Corresponding author). 228: Automated segmentation of the human placenta and uterus with MR imaging using artificial intelligence (AI). American Journal of Obstetrics &amp; Gynecology; 222(1): S158-S159.<\/p>\n \r\n\r\n\r\n\r\n\r\n\t\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/01\/Fei_2020_AJOG_Twickler_Automated_segmentation_Placenta_AI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Ma L, Lu G, Wang D, Qin X, Chen ZG, <strong>Fei BW<\/strong> (Corresponding author). Adaptive deep learning for head and neck cancer detection using hyperspectral imaging. Visual Computing for Industry, Biomedicine, and Art; 2(1): 1-12.<\/p>\n \r\n\r\n\r\n\r\n\r\nLing_2019_VisualComputing\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32190408\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/01\/Fei_2019_VC_Ma_Adaptive_deep_hyperspectral.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1186\/s42492-019-0023-8\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/01\/Ling_2019_VisualComputing_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2020\/01\/Ling_2019_VisualComputing_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Nalawade S, Murugesan GK, Vejdani-Jahromi M, Fisicaro RA, Yogananda GCB, Wagner B, Mickey B, Maher E, Pinho MC, <strong>Fei BW<\/strong>, Madhuranthakam AJ, Maldjian JA. Classification of brain tumor isocitrate dehydrogenase status using MRI and deep learning. Journal of Medical Imaging; 6(4): 046003.<\/p>\n \r\n\r\n\r\n\r\n\r\nNalawade_2019_SPIE\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31824982\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/01\/Fei_2019_SPIE_Nalawade_Classification_isocitrate_dehydrogenase.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/1.JMI.6.4.046003\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/01\/Nalawade_2019_SPIE_BibTeX-1.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/01\/Nalawade_2019_SPIE_EndNote-1.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Shahedi M, Halicek M, Dormer JD, Schuster DM, <strong>Fei BW<\/strong> (Corresponding author). Deep learning-based three-dimensional segmentation of the prostate on computed tomography images. Journal of Medical Imaging;6(2):025003.<\/p>\n \r\n\r\n\r\n\r\n\r\nShahedi_2019_MedImaging\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31065570\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2019_JMI_Shahedi_Deep_learning_Segmentation_Prostate_CT.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/1.JMI.6.2.025003\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Shahedi_2019_MedImaging_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Shahedi_2019_MedImaging_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Halicek M, Little JV, Wang X, Chen AY, <strong>Fei BW<\/strong> (Corresponding author). Optical biopsy of head and neck cancer using hyperspectral imaging and convolution neural networks. Journal of Biomedical Optics;24(3):1-9.<\/p>\n \r\n\r\n\r\n\r\n\r\nHalicek_2019_JBO\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30197462\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2019_JBO_Halicek_Optical_biopsy_Hyperspectral_CNN.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/1.JBO.24.3.036007\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Halicek_2019_JBO_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Halicek_2019_JBO_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p><strong>Fei BW<\/strong>, Callic\u00f3 GM, Morera J, Szolna A, Sarmiento R, Ortega S, Halicek M, Himar F. Surgical aid visualization system for glioblastoma tumor identification based on deep learning and in-vivo hyperspectral images of human patients. Proceedings of SPIE 2019: Image-Guided Procedures, Robotic Interventions, and Modeling.<\/p>\n \r\n\r\n\r\n\r\n\r\nFabelo_2019_SPIE\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31447494\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2019_SPIE_Linte_Surgical_aid_Gliobastoma_Deep_learning_Hyperspectral.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2512569\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/10\/Fabelo_2019_SPIE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Shahedi M, Ma L, Halicek M, Guo R, Zhang G, Schuster DM, Nieh P, Master VA, <strong>Fei BW<\/strong> (Corresponding author). A semiautomatic algorithm for three-dimensional segmentation of the prostate on CT images using shape and local texture characteristics. Proceedings of SPIE: The Internation Society for Optical Engineering;10576.<\/p>\n \r\n\r\n\r\n\r\n\r\nShahedi_2018_MedImaging\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/25087857\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2018_SPIE_Shahedi_Semiautomatic_3D_segmentation_Prostate_CT.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2293195\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Shahedi_2018_MedImaging_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Shahedi_2018_MedImaging_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Shahedi M, Halicek M, Liu L, Zhang Z, Schuster DM, <strong>Fei BW<\/strong> (Corresponding author). A semiautomatic segmentation method for the prostate in magnetic resonance images using local texture classification and statistical shape modeling. The Annual Meeting of the Biomedical Engineering Society.<\/p>\n \r\n\r\n\r\n\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/29611216\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2018_BMES_Shahedi_Semiautomatic_segmentation_Prostate_MRI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2512282\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Shahedi M, Halicek M, Liu L, Zhang Z, Schuster DM, <strong>Fei BW<\/strong> (Corresponding author). A semiautomatic segmentation method for the prostate in magnetic resonance images using local texture classification and statistical shape modeling. Medical Physics;45(6):2527-2541.<\/p>\n \r\n\r\n\r\n\r\n\r\nShahedi_2018_MedPhys\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/29611216\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2018_MedPhys_Shahedi_Semiautomatic_segmentation_Prostate_MRI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2512282\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Shahedi_2018_MedPhys_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Shahedi_2018_MedPhys_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Halicek M, Shahedi M, Wang X, Little JV, Patel M, Griffith CC, El-Diery MW, Chen AY, <strong>Fei BW<\/strong> (Corresponding author). Automated detection of squamous cell carcinoma from head and neck cancer patients in digitized whole-slide histological images using deep learning. The Annual Meeting of the Biomedical Engineering Society.<\/p>\n \r\n\r\n\r\n\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31575946\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Dormer JD, Halicek M, Ma L, Reilly CM, Schreibmann E, <strong>Fei BW<\/strong> (Corresponding author). Convolutional neural networks for the detection of diseased hearts using CT images and left atrium patches. Proceedings of SPIE: The International Society for Optical Engineering;10575.<\/p>\n \r\n\r\n\r\n\r\n\r\nDormer_2018_MedImaging_2\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30197463\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2018_SPIE_Dormer_CNN_Diseased_hearts_CT_Left_atrium.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2293548\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Dormer_2018_MedImaging_2_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Dormer_2018_MedImaging_2_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Dormer JD, Ma L, Halicek M, Reilly CM, Schreibmann E, <strong>Fei BW<\/strong> (Corresponding author). Heart chamber segmentation from CT using convolutional neural networks. Proceedings of SPIE: The International Society for Optical Engineering;10578.<\/p>\n \r\n\r\n\r\n\r\n\r\nDormer_2018_MedImaging\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30197464\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2018_SPIE_Dormer_Heart_chamber_segmentation_CT_CNN.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2293554\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Dormer_2018_MedImaging_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Dormer_2018_MedImaging_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Halicek M, Little JV, Wang X, Patel M, Griffith CC, El-Diery MW, Chen AY, <strong>Fei BW<\/strong> (Corresponding author). Optical biopsy of head and neck cancer using hyperspectral imaging and convolution neural networks. Proceedings of SPIE: The Internation Society for Optical Engineering;10469.<\/p>\n \r\n\r\n\r\n\r\n\r\nHalicek_2018_SPIE\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30891966\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2018_SPIE_Halicek_Optical_biopsy_Head_Neck_Hyperspectral_CNN.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/1.JBO.24.3.036007\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Halicek_2018_SPIE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Halicek_2018_SPIE_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Halicek M, Little JV, Wang X, Patel M, Griffith CC, Chen AY, <strong>Fei BW<\/strong> (Corresponding author). Tumor margin classification of head and neck cancer using hyperspectral imaging and convolutional neural networks. Proceedings of SPIE: The International Society for Optical Engineering;10576.<\/p>\n \r\n\r\n\r\n\r\n\r\nHalicek_2018_MedImaging\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30245540\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2018_SPIE_Halicek_Tumor_margin_Head_Neck_Hyperspectral_CNN.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2293167\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Halicek_2018_MedImaging_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Halicek_2018_MedImaging_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Dormer JD, Guo R, Shen M, Jiang R, Wagner MB, <strong>Fei BW<\/strong> (Corresponding author). Ultrasound segmentation of rat hearts using convolutional neural networks. Proceedings of SPIE: The International Society for Optical Engineering;10580.<\/p>\n \r\n\r\n\r\n\r\n\r\nDormer_2018_MedImaging_1\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30197465\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2018_SPIE_Dormer_Ultrasound_segmentation_Rat_hearts_CNN.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1117\/12.2293558\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Dormer_2018_MedImaging_1_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Dormer_2018_MedImaging_1_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p><strong>Fei BW<\/strong> (Corresponding author), Computer-aided diagnosis of prostate cancer with MRI, Current Opinion in Biomedical Engineering (In Press, <strong>Invited Review<\/strong>)<\/p>\n \r\n\r\n\r\n\r\n\r\nFei_2017_CurrentBME\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/29732440\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2017_CurrentBME_Computer_Diagnosis_Prostate_MRI.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\t[<a target=\"_blank\" href=\"https:\/\/doi.org\/10.1016\/j.cobme.2017.09.009\" rel=\"noopener noreferrer\">DOI<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Fei_2017_CurrentBME_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Fei_2017_CurrentBME_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Tian Z, Liu L, <strong>Fei BW<\/strong>, Deep convolutional neural network for prostate MR segmentation, Proc. SPIE 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, 101351L, March 3, 2017, Orlando, FL.<\/p>\n \r\n\r\n\r\n\r\n\r\nTian_2017_MedImaging\r\n\r\n\t\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/08\/Fei_2017_SPIE_Tian_Deep_CNN_Prostate_segmentation.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Tian_2017_MedImaging_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Tian_2017_MedImaging_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Chen X, Jin J, <strong>Fei BW<\/strong>, Histogram processing-based image enhancement of digital radiography for detection of cardiac calcification, The World Congress of Medical Physics and Biomedical Engineering, Beijing, China, IFMBE Proceedings 2013, 39, 939-942.<\/p>\n \r\n\r\n\r\n\r\n\r\nChen_2013_WorldCongressMedPhys\r\n\r\n\t\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Fei_2013_WorldCongressMedPhys_Chen_Histogram.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Chen_2013_WorldCongressMedPhys_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Chen_2013_WorldCongressMedPhys_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<li>\r\n\r\n<p>Bebek O, Hwang MJ, <strong>Fei BW<\/strong>, Cavusoglu M. Design of a small animal biopsy robot. Proceedings of IEEE Engineering in Medicine and Biology Society 2008; 5601-4. PubMed PMID:19163987.<\/p>\n \r\n\r\n\r\n\r\n\r\nBebek_2008_IEEE\r\n\r\n\t\r\n\r\n<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/19163987\" rel=\"noopener noreferrer\">PubMed<\/a>]<\/span>\r\n\r\n\r\n\t<span class=\"pdf\">[<a target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2008\/01\/Fei_2008_IEEE_EMBS_Design_Animal_Robot.pdf\" rel=\"noopener noreferrer\">PDF<\/a>]<\/span>\r\n\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Bebek_2008_IEEE_BibTeX.txt\" rel=\"noopener noreferrer\">Bibtex<\/a>]\r\n\r\n\r\n\t[<a download target=\"_blank\" href=\"https:\/\/fei-lab.org\/wp-content\/uploads\/2019\/09\/Bebek_2008_IEEE_EndNote.enw\" rel=\"noopener noreferrer\">Endnote<\/a>]\r\n\r\n\r\n\r\n<div style=\"height: 10px; clear: both;\"><\/div>\t\t\r\n\t<\/li>\r\n\r\n<\/ul>\r\n\n<\/div><\/section>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":5146,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[28,2],"tags":[],"class_list":["post-4362","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-main-research-post","category-research"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Artificial Intelligence &#8226; 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