Graph Convolutional Networks for Multi-modality Medical Imaging:
Methods, Architectures, and Clinical Applications
- URL: http://arxiv.org/abs/2202.08916v1
- Date: Thu, 17 Feb 2022 22:03:59 GMT
- Title: Graph Convolutional Networks for Multi-modality Medical Imaging:
Methods, Architectures, and Clinical Applications
- Authors: Kexin Ding, Mu Zhou, Zichen Wang, Qiao Liu, Corey W. Arnold, Shaoting
Zhang, Dimitri N. Metaxas
- Abstract summary: Development of graph convolutional networks (GCNs) has spawned a new wave of research in medical imaging analysis.
GCNs capabilities have spawned a new wave of research in medical imaging analysis with the overarching goal of improving quantitative disease understanding, monitoring, and diagnosis.
- Score: 13.940158397866625
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Image-based characterization and disease understanding involve integrative
analysis of morphological, spatial, and topological information across
biological scales. The development of graph convolutional networks (GCNs) has
created the opportunity to address this information complexity via graph-driven
architectures, since GCNs can perform feature aggregation, interaction, and
reasoning with remarkable flexibility and efficiency. These GCNs capabilities
have spawned a new wave of research in medical imaging analysis with the
overarching goal of improving quantitative disease understanding, monitoring,
and diagnosis. Yet daunting challenges remain for designing the important
image-to-graph transformation for multi-modality medical imaging and gaining
insights into model interpretation and enhanced clinical decision support. In
this review, we present recent GCNs developments in the context of medical
image analysis including imaging data from radiology and histopathology. We
discuss the fast-growing use of graph network architectures in medical image
analysis to improve disease diagnosis and patient outcomes in clinical
practice. To foster cross-disciplinary research, we present GCNs technical
advancements, emerging medical applications, identify common challenges in the
use of image-based GCNs and their extensions in model interpretation,
large-scale benchmarks that promise to transform the scope of medical image
studies and related graph-driven medical research.
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