Generative Adversarial Irregularity Detection in Mammography Images

Abstract

This paper presents a novel method for detection of irregular tissues in mammography images. Previous works solved such irregularity detection tasks with binary classifiers. Here, we propose to detect irregularities by only observing the healthy samples and describe anything largely different from them as irregularity (i.e., unhealthy or cancerous tissues in terms of demographic breast images). This is particularly of great interest as it is very complicated to acquire datasets with all types of cancer cell shapes and tissues for building binary classifiers. Our modeling allows for learning an irregularity detector without any supervising signal from the irregular class. To this end, we propose an architecture with two deep convolutional networks ($\mathbf {R}$ and $\mathbf {M}$) that are trained adversarially. $\mathbf {R}$ learns to Reconstruct regular mammography images by only observing healthy tissues and $\mathbf {M}$ (a Matching network) to detect if its input is healthy or not. The experimental results confirm the reliability and superior performance of our methods for detecting cancer tissues in mammography images in comparison with state-of-the-art irregularity detection methods. The code is available at https://github.com/milad-ahmadi/GAID.