The morphology and distribution of microcalcifications in a cluster are the most important characteristics for radiologists to diagnose breast cancer. However, it is time-consuming and difficult for radiologists to identify these characteristics, and there also lacks of effective solutions for automatic characterization. In this study, we proposed a multi-task deep graph convolutional network (GCN) method for the automatic characterization of morphology and distribution of microcalcifications in mammograms. Our proposed method transforms morphology and distribution characterization into node and graph classification problem and learns the representations concurrently. Through extensive experiments, we demonstrate significant improvements with the proposed multi-task GCN comparing to the baselines. Moreover, the achieved improvements can be related to and enhance clinical understandings. We explore, for the first time, the application of GCNs in microcalcification characterization that suggests the potential of graph learning for more robust understanding of medical images.
翻译:在一项研究中,我们建议采用多任务深图变形网络(GCN)方法,对形态进行自动定性,在乳房图中进行微量变形的分布;我们建议的方法将形态和分布特征定性转换成节点和图表分类问题,同时了解特征。我们通过广泛的实验,展示了与基线比较的拟议多任务GCN的显著改进。此外,所取得的改进可以与临床理解相关并增强临床理解。我们第一次探索了将GCN应用于微量化特征分析的可能性,以显示图表学习对于更强有力地理解医学图像的潜力。