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cs.CV2026

SlideMix: Enhancing Whole Slide Image Analysis via Multimodal Shuffling

Chad Wong, Sicheng Chen, Tianyi Zhang +4

Histopathological whole slide images (WSIs) are central to cancer diagnosis, but their gigapixel scale, tissue heterogeneity, weak slide-level supervision, sparse diagnostic region…

cs.CV2026

EviPathBench: Benchmarking Evidence Acquisition and Reasoning in Vision-Language Models for Whole-Slide Pathology

Dankai Liao, Tianyi Zhang, Yufeng Wu +6

Whole-slide image (WSI) diagnosis requires identifying diagnostically relevant regions, examining them across magnifications, and integrating multi-scale evidence. However, most pa…

cs.CV2026

Enhancing Pathological VLMs with Cross-scale Reasoning

Chi Phan, Tianyi Zhang, Qiaochu Xue +5

Pathological images are inherently multi-scale, requiring pathologists to integrate evidence from global tissue architecture at low magnification to cellular morphology at higher m…

cs.CV2026

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis

Chi Phan, Tianyi Zhang, Yufeng Wu +7

Pathological diagnosis is inherently multi-scale, requiring the integration of global tissue architecture at low magnification with cellular morphology at higher magnification. How…

cs.CV2026

BatMIL: Geometry-Embedding State Space Model for Whole-Slide Image Representation

Enhui Chai, Sicheng Chen, Tianyi Zhang +4

Accurate analysis of histopathological images is critical for disease diagnosis and treatment planning. Whole-slide images (WSIs), which digitize tissue specimens at gigapixel reso…