In the original HoVerNet repo(https://github.com/vqdang/hover_net), they use imgaug(https://github.com/aleju/imgaug) to do data augmentation, which has several differences with MONAI.

• add new_key in Lambda (addressed by Add new_key args in Lambdad #5526)
  Now in Lambdad, if we set overwrite to False, it will not return data after the Lambda function. We may add an argument called new_key, then if it is not None, we can get the data after the Lambda function. And If it is None with overwrite set as False, we just get the behavior as before.
  

  MONAI/monai/transforms/utility/dictionary.py

  Line 1035 in 5332187

  overwrite: whether to overwrite the original data in the input dictionary with lamdbda function output.

• add RandShiftSaturation and RandShiftHue
  This one can be replaced by TorchVisiond this one.
  

  MONAI/monai/transforms/utility/dictionary.py

  Line 1370 in 5332187

  class TorchVisiond(MapTransform):

  
  https://github.com/Project-MONAI/tutorials/blob/f43e771e7cf35427715baa16245e8a12dffe423a/pathology/tumor_detection/ignite/camelyon_train_evaluate.py#L89-L91
• add RandomOrder (addressed by Add RandomOrder #5534)
  When we have a list of transforms, we may want these transforms to do in random order. We can get a reference from imgaug repo.
  https://github.com/aleju/imgaug/blob/0101108d4fed06bc5056c4a03e2bcb0216dac326/imgaug/augmenters/meta.py#L3015-L3017

We may need to enhance our transforms to align with their implementation.
cc @wyli @Nic-Ma