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Found 5 papers, 2 papers with code
BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images
Each WSI, and respective ROIs, are annotated by the consensus of three board-certified pathologists into different lesion categories.
Hierarchical Graph Representations in Digital Pathology
We propose a novel multi-level hierarchical entity-graph representation of tissue specimens to model hierarchical compositions that encode histological entities as well as their intra- and inter-entity level interactions.
Quantifying Explainers of Graph Neural Networks in Computational Pathology
However, popular deep learning methods and explainability techniques (explainers) based on pixel-wise processing disregard biological entities' notion, thus complicating comprehension by pathologists.
HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification
Further, a hierarchical graph neural network (HACT-Net) is proposed to efficiently map the HACT representations to histopathological breast cancer subtypes.
General Classification
Histopathological Image Classification
+1
Towards Explainable Graph Representations in Digital Pathology
Explainability of machine learning (ML) techniques in digital pathology (DP) is of great significance to facilitate their wide adoption in clinics.
