生物医学工程研究2026，Vol.45Issue(1)：36-42,7.DOI:10.19529/j.cnki.1672-6278.2026.01.06
基于多层病理学监督的乳腺癌H&E生成虚拟Ki-67图像研究
Multi-level pathology-guided virtual staining for H&E-to-Ki-67 image generation in breast cancer
摘要
Abstract
Regarding the problem of time-consuming of Ki-67 immunohistochemical staining and weak pairing between H&E and Ki-67 images in breast cancer,we proposed a multi-level pathology-guided supervised generative adversarial network(MPAS-GAN)to generate high-quality virtual Ki-67 images from H&E counterparts to assess the expression distribution of the Ki-67 biomarker.Firstly,the multi-level pathology-guided supervision framework was introduced in MPAS-GAN to solve the tissue misalignment at the macro-feature level,through the confidence-weighted optimal transport alignment.Finally,the diagnostic information at the key patho-logical semantic level was restored through the consistency constraint of Ki-67 pathological information,and the nuclear morphology at the basic cell structure level was retained through the consistency constraint of pathological cell structure.Experimental results on the public MIST and IHC4BC datasets demonstrated that MPAS-GAN significantly outperformed existing state-of-the-art methods across structural similarity index measure(SSIM),peak signal-to-noise ratio(PSNR),Fréchet inception distance(FID),learned percep-tual image patch similarity(LPIPS)metrics.Furthermore,it achieved the highest consistency in the quantitative correlation analysis of Ki-67 positive regions.This research can generate visually realistic and pathologically reliable virtual Ki-67 images,which can effec-tively solve the problem of weakly paired medical image translation,and is expected to provide a more efficient and reliable tool for the digital pathological diagnosis of breast cancer.关键词
虚拟染色/免疫组织化学/弱配对图像/深度学习/监督信息挖掘/反卷积/配准/组织病理学Key words
Virtual staining/Immunohistochemistry/Weakly-paired images/Deep learning/Supervised information mining/De-convolution/Registration/Histopathology分类
医药卫生引用本文复制引用
白少康,陈春晓,陈利海,李阳,王亮,陈柏凯,肖月月..基于多层病理学监督的乳腺癌H&E生成虚拟Ki-67图像研究[J].生物医学工程研究,2026,45(1):36-42,7.基金项目
南京航空航天大学研究生科研与实践创新计划项目(1003-016001). （1003-016001）
