2 citations · 4 across the 4 of their papers we have counts for
4 papers
Semi-Supervised Domain Adaptation with Latent Diffusion for Pathology Image Classification
Tengyue Zhang, Ruiwen Ding, Luoting Zhuang +3
Deep learning models in computational pathology often fail to generalize across cohorts and institutions due to domain shift. Existing approaches either fail to leverage unlabeled…
Predicting ulcer in H&E images of inflammatory bowel disease using domain-knowledge-driven graph neural network
Ruiwen Ding, Lin Li, Rajath Soans +6
Inflammatory bowel disease (IBD) involves chronic inflammation of the digestive tract, with treatment options often burdened by adverse effects. Identifying biomarkers for personal…
Combining Graph Neural Network and Mamba to Capture Local and Global Tissue Spatial Relationships in Whole Slide Images
Ruiwen Ding, Kha-Dinh Luong, Erika Rodriguez +2
In computational pathology, extracting spatial features from gigapixel whole slide images (WSIs) is a fundamental task, but due to their large size, WSIs are typically segmented in…
Improving mitosis detection on histopathology images using large vision-language models
Ruiwen Ding, James Hall, Neil Tenenholtz +1
In certain types of cancerous tissue, mitotic count has been shown to be associated with tumor proliferation, poor prognosis, and therapeutic resistance. Due to the high inter-rate…