MorphoSHAP: Rethinking the Unit of Attribution in Explanation for Deep Visual Models

MorphoSHAP is a model‑agnostic post‑hoc explanation method that uses morphological shapes—derived from the Tree of Shapes—as the units of attribution in a Shapley game. Each shape is characterized by its scale, geometry, and signed contribution, enabling explanations that reveal where evidence lies, what structural type carries it, and how strongly it influences the prediction. The approach offers spatial, textual, and global class‑level explanations, surpasses existing attribution methods on multiple benchmarks, and is preferred by users in a study.

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