3 citations · 3 across the 5 of their papers we have counts for
10 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 Pathology Foundation Model for Gastric Cancer with Real-World Validation
Ling Liang, Jiabo Ma, Zhengyu Zhang +25
Gastric cancer remains a major cause of cancer mortality, yet its histological and molecular heterogeneity complicates diagnosis and risk stratification. General-purpose pathology…
A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model
Zhe Xu, Ziyi Liu, Junlin Hou +13
Multimodal large language models (MLLMs) have emerged as powerful tools for computational pathology, offering unprecedented opportunities to integrate pathological images with lang…
LLM-driven Medical Report Generation via Communication-efficient Heterogeneous Federated Learning
Haoxuan Che, Haibo Jin, Zhengrui Guo +3
LLMs have demonstrated significant potential in Medical Report Generation (MRG), yet their development requires large amounts of medical image-report pairs, which are commonly scat…
PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology
Jiabo Ma, Yingxue Xu, Fengtao Zhou +23
The emergence of pathology foundation models has revolutionized computational histopathology, enabling highly accurate, generalized whole-slide image analysis for improved cancer d…
Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining
Qichen Sun, Zhengrui Guo, Rui Peng +2
Recent advances in computational pathology and artificial intelligence have significantly enhanced the utilization of gigapixel whole-slide images and and additional modalities (e.…