3 citations · 4 across the 8 of their papers we have counts for
8 papers
CytoFormer: A Molecularly Supervised Cell Foundation Model for Histopathology Cell Classification
Jialu Yao, Songhao Li, Alina Yu +1
Identifying cell types directly from routine haematoxylin and eosin (H&E) histology would enable single-cell analysis at scale, but training such models has relied on manual pathol…
CoDiR: Confidence-Guided Diffusion Refinement for Semi-Supervised Histopathology Segmentation
Hoai Nhan Pham, Dang-Nguyen Bui, Le-Van Thai +7
Semi-supervised histopathology segmentation is challenging due to scarce annotations and unreliable pseudo-labels in ambiguous gland regions. To address this problem, we propose Co…
ProBAG: Prototype-Guided Boundary-Aware Graph Diffusion for Weakly Supervised Histopathology Segmentation
Duy-Dong Nguyen, Le-Van Thai, Hoai Nhan Pham +3
Weakly supervised semantic segmentation enables histopathology tissue segmentation from image-level annotations, avoiding costly pixel-level labeling by expert pathologists. Howeve…
Linking spatial biology and clinical histology via Haiku
Yan Cui, Jacob S. Leiby, Wenhui Lei +6
Integrating molecular, morphological, and clinical data is essential for basic and translational biomedical research, yet systematic frameworks for jointly modeling these modalitie…
Adaptive Multi-Scale Integration Unlocks Robust Cell Annotation in Histopathology Images
Yinuo Xu, Yan Cui, Mingyao Li +1
Identifying cell types and subtypes in routine histopathology is fundamental for understanding disease. Existing tile-based models capture nuclear detail but miss the broader tissu…
Pathology-CoT: Learning Visual Chain-of-Thought Agent from Expert Whole Slide Image Diagnosis Behavior
Sheng Wang, Ruiming Wu, Charles Herndon +4
Diagnosing a whole-slide image is an interactive, multi-stage process of changing magnification and moving between fields. Although recent pathology foundation models demonstrated…