12 papers
Democratizing and accelerating AI-driven pathology research through agentic intelligence
Jiabo Ma, Cheng Jin, Yihui Wang +19
Computational pathology has advanced rapidly with the emergence of foundation models, yet widespread adoption remains limited by substantial technical complexity and programming re…
A Multimodal Agentic Pathology Co-pilot via Evidence Grounded Reasoning
Zhe Xu, Zhengyu Zhang, Zhiyuan Cai +26
Pathology is the cornerstone of modern medicine, where accurate decision-making relies heavily on evidence-based practices. While artificial intelligence (AI) has the potential to…
A Clinically Validated Foundation Model for Comprehensive Lung Pathology Interpretation
Zhengrui Guo, Zhengyu Zhang, Jiabo Ma +23
Pathological assessment guides lung cancer diagnosis, treatment selection, and prognostic evaluation, yet current CPath approaches rely on task-specific models for isolated objecti…
Free Lunch in Medical Image Foundation Model Pre-training via Randomized Synthesis and Disentanglement
Yuhan Wei, Yuting He, Linshan Wu +3
Medical image foundation models (MIFMs) have demonstrated remarkable potential for a wide range of clinical tasks, yet their development is constrained by the scarcity, heterogenei…
Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook
Yuan Ma, Junlin Hou, Chao Zhang +4
Learning from noisy labels remains a major challenge in medical image analysis, where annotation demands expert knowledge and substantial inter-observer variability often leads to…
A Versatile Foundation Model for AI-enabled Mammogram Interpretation
Fuxiang Huang, Jiayi Zhu, Yunfang Yu +20
Breast cancer is the most commonly diagnosed cancer and the leading cause of cancer-related mortality in women globally. Mammography is essential for the early detection and diagno…