Train a baseline model from scratch on dcm-zurich-lesions datasets using nnUNet
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valosekj
commented
Feb 2, 2024
Open
5 tasks
…lti-channel training
…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.
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| # 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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This PR contains scripts for training baseline models from scratch on
dcm-zurich-lesionsdatasets using nnUNetv2: