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ORCIDMedAGI@MICCAI 2024: Marrakesh, Morocco
- Zhongying Deng
, Yiqing Shen, Hyunwoo J. Kim, Won-Ki Jeong, Angelica I. Avilés-Rivero, Junjun He, Shaoting Zhang:
Foundation Models for General Medical AI - Second International Workshop, MedAGI 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings. Lecture Notes in Computer Science 15184, Springer 2025, ISBN 978-3-031-73470-0 - Yiqing Shen, Xinyuan Shao, Blanca Inigo Romillo, David Dreizin, Mathias Unberath:
FastSAM-3DSlicer: A 3D-Slicer Extension for 3D Volumetric Segment Anything Model with Uncertainty Quantification. 1-9 - Gustav Bredell, Marcel Fischer, Przemyslaw Szostak, Samaneh Abbasi-Sureshjani, Alvaro Gomariz:
The Importance of Downstream Networks in Digital Pathology Foundation Models. 10-19 - Zhennong Chen, Sekeun Kim, Hui Ren, Quanzheng Li, Xiang Li:
Temporal-Spatial Adaptation of Promptable SAM Enhance Accuracy and Generalizability of Cine CMR Segmentation. 20-29 - Stefano Woerner, Christian F. Baumgartner:
Navigating Data Scarcity Using Foundation Models: A Benchmark of Few-Shot and Zero-Shot Learning Approaches in Medical Imaging. 30-39 - Woojin Chung, Yujun Park, Yonnho Nam:
AutoEncoder-Based Feature Transformation with Multiple Foundation Models in Computational Pathology. 40-49 - Felix Küper, Sergi Pujades:
OSATTA: One-Shot Automatic Test Time Augmentation for Domain Adaptation. 50-60 - Mélanie Gaillochet, Christian Desrosiers, Hervé Lombaert:
Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision. 61-70 - Hao Xu, Tengfei Xue, Dongnan Liu, Fan Zhang, Carl-Fredrik Westin, Ron Kikinis, Lauren J. O'Donnell, Weidong Cai:
SAT-Morph: Unsupervised Deformable Medical Image Registration Using Vision Foundation Models with Anatomically Aware Text Prompt. 71-80 - Yiqing Shen, Guannan He, Mathias Unberath:
Promptable Counterfactual Diffusion Model for Unified Brain Tumor Segmentation and Generation with MRIs. 81-90 - Hareem Nisar, Syed Muhammad Anwar, Zhifan Jiang, Abhijeet Parida, Ramon Sánchez-Jacob, Vishwesh Nath, Holger R. Roth, Marius George Linguraru:
D-Rax: Domain-Specific Radiologic Assistant Leveraging Multi-modal Data and eXpert Model Predictions. 91-102 - Lara Siblini, Gustavo Andrade-Miranda, Kamilia Taguelmimt, Dimitris Visvikis, Julien Bert:
Optimal Prompting in SAM for Few-Shot and Weakly Supervised Medical Image Segmentation. 103-112 - Yaxiong Chen, Chuang Du, Chunlei Li, Jingliang Hu, Yilei Shi, Shengwu Xiong, Xiao Xiang Zhu, Lichao Mou:
UniCrossAdapter: Multimodal Adaptation of CLIP for Radiology Report Generation. 113-123 - Yulin Wang, Honglin Xiong, Yi Xie, Jiameng Liu, Qian Wang, Qian Liu, Dinggang Shen:
TUMSyn: A Text-Guided Generalist Model for Customized Multimodal MR Image Synthesis. 124-133 - Joseph Bae, Xueqi Guo, Halid Ziya Yerebakan, Yoshihisa Shinagawa, Sepehr Farhand:
SAMU: An Efficient and Promptable Foundation Model for Medical Image Segmentation. 134-142 - Mingrui Zhuang, Rui Xu, Qinhe Zhang, Ailian Liu, Xin Fan, Hongkai Wang:
Anatomical Embedding-Based Training Method for Medical Image Segmentation Foundation Models. 143-152 - Maxime Zanella, Fereshteh Shakeri, Yunshi Huang, Houda Bahig, Ismail Ben Ayed:
Boosting Vision-Language Models for Histopathology Classification: Predict All at Once. 153-162 - David Bani-Harouni, Nassir Navab, Matthias Keicher:
MAGDA: Multi-agent Guideline-Driven Diagnostic Assistance. 163-172
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