4 citations · 7 across the 4 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…
Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology
Wenhao Tang, Fengtao Zhou, Sheng Huang +3
Multiple instance learning (MIL) is the most widely used framework in computational pathology, encompassing sub-typing, diagnosis, prognosis, and more. However, the existing MIL pa…
Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classification
Wenhao Tang, Sheng Huang, Xiaoxian Zhang +3
The whole slide image (WSI) classification is often formulated as a multiple instance learning (MIL) problem. Since the positive tissue is only a small fraction of the gigapixel WS…
Boosting Multi-Label Image Classification with Complementary Parallel Self-Distillation
Jiazhi Xu, Sheng Huang, Fengtao Zhou +3
Multi-Label Image Classification (MLIC) approaches usually exploit label correlations to achieve good performance. However, emphasizing correlation like co-occurrence may overlook…