@article{WANG2023, 
author = {Hongyu WANG and Tianyi ZHU and Jun FENG and Songtao DING and Pingping WANG and Baoying CHEN},
title = {Breast MRI image generation method based on generative adversarial network},
year = {2023},
journal = {Journal of Northwest University (Natural Science Edition)},
volume = {53},
number = {3},
pages = {348-358},
keywords = {generative adversarial network, breast MRI image generation, attention mechanism, gradient normalisation},
url = {https://www.sciopen.com/article/10.16152/j.cnki.xdxbzr.2023-03-004},
doi = {10.16152/j.cnki.xdxbzr.2023-03-004},
abstract = {Breast cancer MRI data often have the problem of different sequences missing from the same patient due to different acquisition methods, different equipment or patients and other self-inflicted reasons in different hospitals. These methods often require different MRI sequences to appear in pairs, making it difficult to handle missing data. In addition, these methods tend to focus on the quality of the entire image, and lack the ability to monitor the quality of the focal area, which is more valuable for disease diagnosis. In this paper, we design a new feature-enhanced dual-attention aligned generative adversarial network, DA-RegGAN, inspired by the adaptive alignment of image spatial distribution in RegGAN. The gradient normalization constraint is added to the discriminator, which mainly solves the phenomenon that the network is unstable and prone to pattern running, and enables the network to generate a global map containing clearer lesion details. In this paper, ablation experiments, comparison experiments between different image generation algorithms and tumour classification experiments are carried out on 1697 breast case data to further validate the effectiveness of the method in this paper. Compared with the original RegGAN, the global image generation quality and local lesion image generation quality were both improved, with the local image quality improved by 0.518 and SSIM improved by 0.021 compared with the original PSNR; the global image quality improved by 0.584 and SSIM improved by 0.020 compared with the original PSNR.}
}