7 papers
From Patches to Evidence Balls: Class-Conditioned Evidence Retrieval for Few-Shot Whole Slide Image Classification
Di Zhang, Li Zhang, Jiashuai Liu +9
Whole slide image (WSI) classification is an evidence-driven task, where diagnostic cues are often sparse, spatially organized, and class-dependent. Existing MIL and vision-languag…
AGE-MIL: Anchor-Guided Evidence Learning for Patient-Level Prediction
Jiawei Niu, Jian Chen, Di Zhang +8
Existing computational pathology methods predominantly operate within whole-slide image (WSI)-level multiple instance learning (MIL) paradigms, while patient-level modeling remains…
Thinking in Scales: Accelerating Gigapixel Pathology Image Analysis via Adaptive Continuous Reasoning
Jiusong Ge, Yingkang Zhan, Wenjie Zhao +13
Traditional whole slide image (WSI) analysis methods typically rely on the multiple instance learning (MIL) paradigm, which extracts patch-level features at high magnification and…
PathNavigate: A Training-Free Pathology Agent with Surprise-Guided Scan and Shared Slide Memory for Whole-Slide Image VQA
Chunze Yang, Qidong Liu, Wenjie Zhao +10
Whole-slide image visual question answering (WSI-VQA) frames pathology as an extreme-context search problem: to answer a free-form clinical query, a system must first navigate a gi…
CARE: A Molecular-Guided Foundation Model with Adaptive Region Modeling for Whole Slide Image Analysis
Di Zhang, Zhangpeng Gong, Xiaobo Pang +14
Foundation models have recently achieved impressive success in computational pathology, demonstrating strong generalization across diverse histopathology tasks. However, existing m…
The Butterfly Effect in Pathology: Exploring Security in Pathology Foundation Models
Jiashuai Liu, Yingjia Shang, Yingkang Zhan +7
With the widespread adoption of pathology foundation models in both research and clinical decision support systems, exploring their security has become a critical concern. However,…