Tag
SlideBank is a training-free framework for whole-slide image reasoning in pathology, using a persistent hierarchical evidence bank to improve consistency and reduce inference costs.
This review formalizes Explainable AI (XAI) methods in computational pathology by introducing definitions, a taxonomy, and task-driven recommendations to address clinical adoption challenges.
GigaPath-Flash and GigaTIME-Flash are efficient pathology foundation models that reduce computational demands while maintaining performance, enabling larger-scale studies in computational pathology.
Introduces AdaSurvMamba, an adaptive framework for multimodal survival analysis that uses a dual-scale importance-aware reconstruction module and semantic aggregation scanning to improve integration of whole slide images and genomic profiles, achieving consistent gains across five TCGA cohorts.
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.