Molecularly guided pathology foundation models (PFMs) exploit transcriptomic or proteomic information to enrich whole-slide image (WSI) representations, yet effectively leveraging large standalone molecular corpora remains challenging. First, existing molecular foundation models encode protein sequences or single-cell...
Di Zhang, Zhang-Peng Gong, Jia-Shuai Liu et al.· 0 citations
Due to the gigapixel-scale nature of whole-slide images (WSIs), weakly supervised WSI analysis is commonly formulated as a multiple instance learning (MIL) problem, where patch-level features are aggregated into slide-level representations. However, diagnostic and prognostic evidence often arises from spatially coheren...
Lei Wu, Jia-Shuai Liu, Di Zhang et al.· 0 citations
EviBall reformulates few-shot WSI classification as structured evidence retrieval and competition among candidate classes, and reduces the reliance on learning an unconstrained global aggregation mechanism from scarce slide-level labels.
Di Zhang, Li Zhang, Jia-Shuai Liu et al.· 0 citations
This Review examines volumetric foundation models, language alignment and compression strategies, and agentic systems that extend MLLMs through planning, tools, memory, and workflow interaction, and introduces a Claim-Design-Validation framework to assess whether technical, workflow, and clinical claims are matched by...
Zan-Ting Ye, Shengyuan Liu, Xin Liu et al.· 0 citations
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