论文标题
用于多模式医学成像的图形卷积网络：方法，架构和临床应用
Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications
论文作者
论文摘要
基于图像的表征和疾病的理解涉及对生物学量表的形态，空间和拓扑信息的综合分析。图形卷积网络（GCN）的开发创造了通过图形驱动体系结构来解决此信息复杂性的机会，因为GCN可以以显着的灵活性和效率执行特征聚合，交互和推理。这些GCN能力在医学成像分析中产生了新的研究，其总体目标是改善定量疾病的理解，监测和诊断。然而，对于设计重要的图像到绘画转换，用于多模式医学成像，并获得对模型解释和增强临床决策支持的见解，仍然存在艰巨的挑战。在这篇综述中，我们在医学图像分析的背景下介绍了GCNS的最新发展，包括放射学和组织病理学的成像数据。我们讨论图形网络体系结构在医学图像分析中的快速增长，以改善临床实践中的疾病诊断和患者结局。为了培养跨学科研究，我们提出了GCN的技术进步，新兴的医疗应用，确定了基于图像的GCN使用及其在模型解释中扩展的共同挑战，大规模的基准有望改变医学图像研究范围和相关的图形驱动医学研究的范围。
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.
