Multi-task Graph Convolutional Neural Network for Calcification Morphology and Distribution Analysis in Mammograms

• DOI: 10.48550/arxiv.2105.06822
• Publication Date: 2021-01-01
• Authors: Hao Du; Melissa Min-Szu Yao; Liangyu Chen; Wing P. Chan; Mengling Feng
• Keywords: Microcalcification; Characterization

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 difficult identify these characteristics, there also lacks effective solutions automatic characterization. In this study, we proposed multi-task deep graph convolutional network (GCN) method characterization mammograms. Our transforms into node classification problem learns representations concurrently. Through extensive experiments, demonstrate significant improvements with GCN comparing baselines. Moreover, achieved can be related enhance clinical understandings. We explore, first time, application GCNs microcalcification that suggests potential learning more robust understanding medical images.