计算机与现代化Issue(3)：92-96,104,6.DOI:10.3969/j.issn.1006-2475.2024.03.015
基于生成对抗网络的乳腺癌免疫组化图像生成
Breast Cancer Immunohistochemical Image Generation Based on Generative Adversarial Network
摘要
Abstract
Breast cancer is a dangerous malignant tumor.In medicine,human epidermal growth factor receptor 2(HER2)levels are needed to determine the aggressiveness of breast cancer in order to develop a treatment plan,this requires immunohistochemi-cal(IHC)staining of the tissue sections.In order to solve the problem that IHC staining is expensive and time-consuming,firstly,a HER2 prediction network based on mixed attention residual module is proposed,and a CBAM module is added to the residual module,so that the network can focus on learning at the spatial and channel levels.The prediction network could di-rectly predict HER2 level from HE stained sections,and the prediction accuracy reached more than 97.5%,which increased by more than 2.5 percentage points compared with other networks.Subsequently,a multi-scale generative adversarial network is proposed,which uses ResNet-9blocks with mixed attention residuals module as generator and PatchGan as discriminator and self-defines multi-scale loss function.This network can directly generate simulated IHC slices from HE stained slices.At low HER2 level,SSIM and PSNR between the generated image and the real image are 0.498 and 24.49 dB.关键词
生成对抗网络/图像处理/混合注意力机制/类别预测Key words
generative adversarial network/image processing/mixed attention mechanism/category prediction分类
信息技术与安全科学引用本文复制引用
卢梓菡,张东,杨艳,杨双..基于生成对抗网络的乳腺癌免疫组化图像生成[J].计算机与现代化,2024,(3):92-96,104,6.基金项目
国家重点研发计划项目(2011CB707900) （2011CB707900）
广西高校中青年教师科研基础能力提升项目(2019KY0816) （2019KY0816）
