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

TL;DR

This paper introduces a multi-task graph convolutional neural network that automatically characterizes microcalcification morphology and distribution in mammograms, improving diagnostic efficiency and clinical understanding.

Contribution

It is the first application of GCNs for microcalcification characterization, transforming the problem into node and graph classification for concurrent learning.

Findings

Significant performance improvements over baselines

Enhanced clinical interpretability of microcalcification features

Demonstrated potential of graph learning in medical image analysis

Abstract

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.