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This paper systematically evaluates foundation model representations for multimodal cancer analysis, benchmarking unimodal and multimodal fusion strategies on real-world cohorts, and assessing trustworthiness via conformal prediction.
PathoSage introduces a three-stage framework for pathology multimodal reasoning that separates knowledge retrieval, evidence collection, and evidence adjudication to reduce hallucinations and handle conflicting evidence, featuring a training-free Beta-Bernoulli experience system for modeling tool reliability.
This paper proposes TopoMamSurv, a Graph Mamba framework for whole-slide image survival analysis that uses topology-aware ordering to address Mamba's sensitivity to input order, and incorporates bidirectional Mamba and GCN for spatial context modeling.