SlideMix is proposed, a model-agnostic multimodal augmentation framework for MIL-based WSI analysis that improves accuracy and generalization in most settings and compares favorably with established augmentation baselines, providing a simple plug-and-play approach for more robust and scalable digital pathology models.
Chad Wong, Si-Cheng Chen, Tian-Yi Zhang et al.· 0 citations
PathA-gentBench is introduced, a benchmark for evaluating evidence-seeking vision-language models (VLMs) across four complementary capabilities: image-to-text matching for evidence interpretation, text-to-image retrieval for evidence verification, diagnostic-region localization for evidence acquisition, and multi-scale...
This work reviews agentic systems in computational pathology using an operational taxonomy based on dynamic control flow, inference-time tool selection, and knowledge integration to emphasize validation scope, computational cost, workflow integration, hallucination and security risks, regulatory requirements, patient p...
Xin-Yu Lu, Qian-Kun Li, Yak Gao et al.· Journal of Translational Med...· 1 citation
A benchmark and training framework for shortcut-resistant cross-scale pathology reasoning, and an Adversarial Text-only Screening strategy for semantic reasoning questions and a Structure-controlled Distractor Sampling strategy for visual grounding questions, encouraging models to rely on cross-scale visual evidence.
Chi Phan, Tian-Yi Zhang, Yu-Feng Wu et al.· arXiv.org· 1 citation
EviPathBench is introduced, a benchmark for evaluating evidence acquisition and reasoning in vision-language models (VLMs) for whole-slide pathology and reveals a pronounced gap between reasoning over curated evidence and acquiring it from WSIs.
Dan-Kai Liao, Tian-Yi Zhang, Yu-Feng Wu et al.· 3 citations
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