20 citations · 22 across the 4 of their papers we have counts for
4 papers
Integrative Graph-Transformer Framework for Histopathology Whole Slide Image Representation and Classification
Zhan Shi, Jingwei Zhang, Jun Kong +1
In digital pathology, the multiple instance learning (MIL) strategy is widely used in the weakly supervised histopathology whole slide image (WSI) classification task where giga-pi…
Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning
Saarthak Kapse, Srijan Das, Jingwei Zhang +4
We propose DiRL, a Diversity-inducing Representation Learning technique for histopathology imaging. Self-supervised learning techniques, such as contrastive and non-contrastive app…
SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology
Jingwei Zhang, Ke Ma, Saarthak Kapse +4
Semantic segmentations of pathological entities have crucial clinical value in computational pathology workflows. Foundation models, such as the Segment Anything Model (SAM), have…
Gigapixel Whole-Slide Images Classification using Locally Supervised Learning
Jingwei Zhang, Xin Zhang, Ke Ma +4
Histopathology whole slide images (WSIs) play a very important role in clinical studies and serve as the gold standard for many cancer diagnoses. However, generating automatic tool…