Comments (1)
It's always difficult to say what features, exactly, the segmentation model is using the make its predictions, but your results seem to suggest that enhancement is an important feature. This doesn't surprise me too much since the kidneys enhance so conspicuously, especially in the arterial contrast phase.
Of course, in the context of the KiTS21 training set, non-contrast CT scans are "out of distribution" and so it's difficult to reason about expected performance on that population. But one thing that might help is to train on other contrast phases as well, if not non-contrast. The KiTS23 dataset includes cases in the nephrogenic phase, which might force the model to learn through some of the aterial-dependent features its using.
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Related Issues (20)
- Can't get imaging.nii.gz after run get_imaging.py HOT 2
- ``evaluation_prediction.py`` gets me an error. HOT 1
- python3 kits21/starter_code/get_imaging.py HOT 5
- ModuleNotFoundError: No module named 'requests' HOT 2
- I need a CT scan kidney masses; chromophobe and oncocytoma kidney tumors HOT 4
- bash script downloading only imaging HOT 2
- get_imaging.py experience issue HOT 4
- kidney class kits19 vs kits21 HOT 3
- Strange image.nii.gz HOT 1
- RAW annotation HOT 3
- Dataset Description HOT 1
- Removal of Cyst Label from segmentation HOT 1
- total process HOT 1
- Connection Error while running the get_imaging.py file HOT 1
- meta data in KiTS nii HOT 2
- case_00205 Cyst Instance 1 and Tumor Instance 3 segment the same region HOT 3
- Inconsistent results on Baseline model HOT 4
- KiTS21 papers no longer available on openreview HOT 2
- How to split train and test data? HOT 1
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