[HTML][HTML] Graph neural networks in histopathology: Emerging trends and future directions
Histopathological analysis of whole slide images (WSIs) has seen a surge in the utilization
of deep learning methods, particularly Convolutional Neural Networks (CNNs). However …
of deep learning methods, particularly Convolutional Neural Networks (CNNs). However …
HECLIP: Histology-Enhanced Contrastive Learning for Imputation of Transcriptomics Profiles
Histopathology, particularly hematoxylin and eosin (H\&E) staining, plays a critical role in
diagnosing and characterizing pathological conditions by highlighting tissue morphology …
diagnosing and characterizing pathological conditions by highlighting tissue morphology …
PathOmCLIP: Connecting tumor histology with spatial gene expression via locally enhanced contrastive learning of Pathology and Single-cell foundation model
Tumor morphological features from histology images are a cornerstone of clinical pathology,
diagnostic biomarkers, and basic cancer biology research. Spatial transcriptomics, which …
diagnostic biomarkers, and basic cancer biology research. Spatial transcriptomics, which …