activity
20242026
collaborators

15 papers

cs.CV2026

DeCo-MIL: Debiased Counterfactual Reasoning for Long-Tailed Whole Slide Image Analysis

Xiaoxiao Li, Xitong Ling, Jiawen Li +6

Multiple instance learning (MIL) is widely used for weakly supervised whole slide image (WSI) analysis. However, under long-tailed distributions, MIL-based WSI analysis faces a nes…

cs.AI2026

GlobalDentBench: A Multinational Benchmark for Evaluating LLM Clinical Reasoning in Dentistry with Expert Calibration

Junjie Zhao, Jingyi Liang, Zhenyang Cai +22

While large language models (LLMs) hold transformative potential for medicine, their reasoning robustness and safety in real-world clinical scenarios remain critically underexplore…

cs.AI2026

Agentifying Patient Dynamics within LLMs through Interacting with Clinical World Model

Minghao Wu, Yuting Yan, Zhenyang Cai +9

Sepsis management in the ICU requires sequential treatment decisions under rapidly evolving patient physiology. Although large language models (LLMs) encode broad clinical knowledg…

cs.CV2026

Beyond ViT Tokens: Masked-Diffusion Pretrained Convolutional Pathology Foundation Model for Cell-Level Dense Prediction

Weiming Chen, Xitong Ling, Zhenyang Cai +5

Cell-level dense prediction is central to computational pathology, but remains challenging due to fine-grained histological structures, strong domain shifts, and costly dense annot…

cs.AI2026

MicroVerse: A Preliminary Exploration Toward a Micro-World Simulation

Rongsheng Wang, Minghao Wu, Hongru Zhou +4

Recent advances in video generation have opened new avenues for macroscopic simulation of complex dynamic systems, but their application to microscopic phenomena remains largely un…

eess.IV2026

To What Extent Do Token-Level Representations from Pathology Foundation Models Improve Dense Prediction?

Weiming Chen, Xitong Ling, Xidong Wang +10

Pathology foundation models (PFMs) have rapidly advanced and are becoming a common backbone for downstream clinical tasks, offering strong transferability across tissues and instit…