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ZIO is proposed, a training-free, multimodal OOD detector for pathology WSIs that leverages vision--language pathology foundation models (FMs) and consistently outperforms both unimodal prototypes and 40 state-of-the-art OOD methods.
Across four external evaluations in colon, renal, lung, and glioblastoma cohorts, MIST improves external C-index over standard fusion baselines in the primary comparisons, which support genomic-guided histology attention as a compact and effective strategy for multimodal oncology outcome prediction.
This review aims to formalize XAI methods in CompPath through the introduction of a pathology-centric vocabulary comprising seven core terms and establishment of a task-driven framework that maps five clinical questions to recommended methods, method evaluation, and deployment context.
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