Hierarchical Cell-to-Tissue Graph Representations for Breast Cancer Subtyping in Digital Pathology
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Cancer diagnosis and prognosis for a tissue specimen are heavily influenced by the phenotype and topological distribution of the constituting histological entities. Thus, adequate tissue representation by encoding the histological entities, and quantifying the relationship between the tissue representation and tissue functionality is imperative for computer aided cancer patient care. To this end, several approaches have leveraged cell-graphs, that encode cell morphology and organization, to denote the tissue information, and utilize graph theory and machine learning to map the representation to tissue functionality. Though cellular information is crucial, it is incomplete to comprehensively characterize the tissue. Therefore, we consider a tissue as a hierarchical composition of multiple types of histological entities from fine to coarse level, that depicts multivariate tissue information at multiple levels. We propose a novel hierarchical entity-graph representation to depict a tissue specimen, which encodes multiple pathologically relevant entity types, intra- and inter-level entity-to-entity interactions. Subsequently, a hierarchical graph neural network is proposed to operate on the entity-graph representation to map the tissue structure to tissue functionality. Specifically, we utilize cells and tissue regions in a tissue to build a HierArchical Cell-to-Tissue (HACT) graph representation, and HACT-Net, a graph neural network, to classify histology images. As part of this work, we propose the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of Haematoxylin Eosin stained breast tumor regions-of-interest, to evaluate and benchmark our proposed methodology against pathologists and state-of-the-art computer-aided diagnostic approaches. Thorough comparative assessment and ablation studies demonstrated the superior classification efficacy of the proposed methodology.
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