Dielectric Breast Phantoms by Generative Adversarial Network.
Dielectric Breast Phantoms by Generative Adversarial Network.
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DOI：
10.1109/tap.2021.3121149
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发表时间：
2022-08
影响因子：
5.7
通讯作者：
Zhou, Beibei
中科院分区：
文献类型：
--
作者：
Shao, Wenyi;Zhou, Beibei
关键词：
为了进行基于机器学习的微波乳腺成像（MBI）研究，需要大量的数字介质乳腺模型作为训练数据（ground truth），但从实践中很难实现。虽然已经开发了一些用于研究目的的介电乳房体模，但是数量和多样性是有限的，并且远远不足以开发用于MBI的鲁棒ML算法。本文提出了一种神经网络方法来生成与真实的乳房模型相似的二维虚拟乳房模型，该模型可用于未来基于ML的MBI的开发。生成的幻影与训练中使用的幻影相似但不同。每个体模由几个图像组成，每个图像代表乳房图中介电参数的分布。对10，000个生成的幻影进行统计分析，以研究生成网络的性能。通过生成网络，可以生成无限数量的具有更多变化的乳房图像，因此基于ML的MBI将更易于部署。
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 is far inadequate to develop a robust ML algorithm for MBI. This paper presents a neural network method to generate 2D 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. Statistical analysis was performed over 10,000 generated phantoms to investigate the performance of the generative network. With the generative network, one may generate unlimited number of breast images with more variations, so the ML-based MBI will be more ready to deploy.
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