AI RESEARCH
Efficient KernelSHAP Explanations for Patch-based 3D Medical Image Segmentation
arXiv CS.AI
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ArXi:2604.11775v1 Announce Type: cross Perturbation-based explainability methods such as KernelSHAP provide model-agnostic attributions but are typically impractical for patch-based 3D medical image segmentation due to the large number of coalition evaluations and the high cost of sliding-window inference.