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Train a baseline model from scratch on dcm-zurich-lesions datasets using nnUNet #2

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@valosekj valosekj commented Feb 2, 2024

This PR contains scripts for training baseline models from scratch on dcm-zurich-lesions datasets using nnUNetv2:

  • region-based model: #1
  • multi-channel model: #5

…dataset_name_id_conversion.py':

"raise RuntimeError("More than one dataset name found for dataset id %d. Please correct that."
…ilities/dataset_name_id_conversion.py':"

This reverts commit 73b9614.

# Needed for finding the files correctly. IMPORTANT! File endings must match between images and segmentations!
json_dict['file_ending'] = ".nii.gz"

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Just to be consistent with latest findings about axes swaps -- maybe we should specify the IO reader ?

json_dict['overwrite_image_reader_writer'] = "NibabelIO"

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Good point! I've been thinking about that too! My only question is: will models with different IO readers (Nibabel/SimpleITK) be comparable?

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2 participants