Implement User-Defined Feature Extractor Support#17
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adas2125
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Jan 20, 2024
| df['id_patient'], df['type'] = zip(*df['file'].apply(extract_tcga_info)) | ||
| else: | ||
| #TODO: This behavior seems dangerous. Shouldn't we have a separated script | ||
| #and describe the input files definition so we don't need to update code |
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Yes I agree, I think we can define id_patient in the dataset class itself.
adas2125
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Jan 20, 2024
| # (2, 3, 5) | ||
| # Path to the pickle file containing metadata for the .svs files | ||
| # Example: meta/dhmc_rcc_svs.pickle | ||
| # TODO: This is a saving path, right? We should clarify that this is not input. |
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Yes, a meta-file is created by 01_get_svs_meta.py and then is used by other pre-processing scripts such as 02_patch_extraction.py
adas2125
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Jan 20, 2024
| # (2) | ||
| # Mask magnification for color filtering during patch extraction. | ||
| # A lower magnification will run faster but will be less precise | ||
| #TODO: Ask why this value? |
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I am not sure. Maybe we should give the user a choice to set this value themselves?
adas2125
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The changes look good. I can work on handling some of the TODO comments as well.
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This PR introduces the capability to load custom feature extractors in our model.
Usage: Set the pretrained model path in config.patch.pretrained_model_path. The system handles integration with the base model architecture.