Author

Di Zhang

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Preprint Sep 2026

Can Protein-Derived Knowledge Improve Pathology Foundation Models?

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

#artificial intelligence Preprint Sep 2026

Modeling Whole-Slide Images as Dynamic Tumor Microenvironment Fields

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

Preprint Aug 2026

From Patches to Evidence Balls: Class-Conditioned Evidence Retrieval for Few-Shot Whole Slide Image Classification

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