5 papers
Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development
Zhongying Deng, Cheng Tang, Ziyan Huang +124
Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high-quality datasets. However, in…
Visual Instruction Pretraining for Domain-Specific Foundation Models
Yuxuan Li, Yicheng Zhang, Wenhao Tang +4
Modern computer vision is converging on a closed loop in which perception, reasoning and generation mutually reinforce each other. However, this loop remains incomplete: the top-do…
Revisiting Data Challenges of Computational Pathology: A Pack-based Multiple Instance Learning Training Framework
Wenhao Tang, Heng Fang, Ge Wu +2
Computational pathology (CPath) digitizes pathology slides into whole slide images (WSIs), enabling analysis for critical healthcare tasks such as cancer diagnosis and prognosis. H…
Revisiting End-to-End Learning with Slide-level Supervision in Computational Pathology
Wenhao Tang, Rong Qin, Heng Fang +4
Pre-trained encoders for offline feature extraction followed by multiple instance learning (MIL) aggregators have become the dominant paradigm in computational pathology (CPath), b…
Multiple Instance Learning Framework with Masked Hard Instance Mining for Gigapixel Histopathology Image Analysis
Wenhao Tang, Sheng Huang, Heng Fang +3
Digitizing pathological images into gigapixel Whole Slide Images (WSIs) has opened new avenues for Computational Pathology (CPath). As positive tissue comprises only a small fracti…