Dielectric Breast Phantoms by Generative Adversarial Network

Abstract

In order to conduct the research of machine learning (ML)-based microwave breast imaging (MBI), a large number of digital dielectric breast phantoms that can be used as training data (ground truth) are required but are difficult to be achieved from practice. Although a few dielectric breast phantoms have been developed for research purpose, the number and the diversity are limited and are far inadequate to develop a robust ML algorithm for MBI. This article presents a neural network method to generate 2-D virtual breast phantoms that are similar to the real ones, which can be used to develop ML-based MBI in the future. The generated phantoms are similar but are different from those used in training. Each phantom consists of several images with each representing the distribution of a dielectric parameter in the breast map. A statistical analysis was performed over 10 000 generated phantoms to investigate the performance of the generative network. With the generative network, one may generate an unlimited number of breast images with more variations, so the ML-based MBI will be more ready to deploy.