An optimized KernelSHAP method for 3D medical image segmentation restricts computation to ROI and receptive fields, uses patch logit caching for 15-30% savings, and compares organ units versus supervoxels for clinically interpretable attributions.
Aggregated Attributions for Explanatory Analysis of 3D Segmentation Models
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Efficient KernelSHAP Explanations for Patch-based 3D Medical Image Segmentation
An optimized KernelSHAP method for 3D medical image segmentation restricts computation to ROI and receptive fields, uses patch logit caching for 15-30% savings, and compares organ units versus supervoxels for clinically interpretable attributions.