1 citations · 1 across the 3 of their papers we have counts for
4 papers
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…
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…
SAM-MIL: A Spatial Contextual Aware Multiple Instance Learning Approach for Whole Slide Image Classification
Heng Fang, Sheng Huang, Wenhao Tang +2
Multiple Instance Learning (MIL) represents the predominant framework in Whole Slide Image (WSI) classification, covering aspects such as sub-typing, diagnosis, and beyond. Current…