Multi-task Graph Convolutional Neural Network for Calcification Morphology and Distribution Analysis in Mammograms
This addresses the time-consuming and difficult task for radiologists in analyzing microcalcifications, with potential to enhance clinical understanding, though it is incremental as it applies existing graph learning methods to a new medical domain.
The paper tackled the problem of automatically characterizing the morphology and distribution of microcalcifications in mammograms for breast cancer diagnosis, proposing a multi-task graph convolutional network that achieved significant improvements over baselines.
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.