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Image-based characterization and disease understanding involve integrativeanalysis of morphological, spatial, and topological information acrossbiological scales. The development of graph convolutional networks (GCNs) hascreated the opportunity to address this information complexity via graph-drivenarchitectures, since GCNs can perform feature aggregation, interaction, andreasoning with remarkable flexibility and efficiency. These GCNs capabilitieshave spawned a new wave of research in medical imaging analysis with theoverarching goal of improving quantitative disease understanding, monitoring,and diagnosis. Yet daunting challenges remain for designing the importantimage-to-graph transformation for multi-modality medical imaging and gaininginsights into model interpretation and enhanced clinical decision support. Inthis review, we present recent GCNs developments in the context of medicalimage analysis including imaging data from radiology and histopathology. Wediscuss the fast-growing use of graph network architectures in medical imageanalysis to improve disease diagnosis and patient outcomes in clinicalpractice. To foster cross-disciplinary research, we present GCNs technicaladvancements, emerging medical applications, identify common challenges in theuse of image-based GCNs and their extensions in model interpretation,large-scale benchmarks that promise to transform the scope of medical imagestudies and related graph-driven medical research.

Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications