深度学习在乳腺肿瘤病理图像分析中的应用  

Application of deep learning in pathological image analysis of breast tumors

作　　者：李冠鹏 翟羽佳 张晓丽[1] 张魁星[1] 薛丹 LI Guanpeng;ZHAI Yujia;ZHANG Xiaoli;ZHANG Kuixing;XUE Dan(College of Intelligence and Information Engineering,Shandong University of Traditional Chinese Medicine,Jinan 250355;Department of Medical Engineering,The First Affiliated Hospital of Shandong First Medical University(Shangdong Qianfoshan Hospital),Jinan 250014)

机构地区：[1]山东中医药大学智能与信息工程学院,济南250355 [2]山东第一医科大学第一附属医院(山东省千佛山医院)医学工程部,济南250014

出　　处：《北京生物医学工程》2025年第1期81-89,共9页Beijing Biomedical Engineering

基　　金：国家自然科学基金(61872225);山东省自然科学基金(ZR2020ZD44、ZR2020QF043);国家卫生健康委医院管理研究所项目(GYZ2023SZ15)资助。

摘　　要：乳腺癌作为女性最高发的恶性肿瘤之一,在全球范围内对女性健康构成严重威胁。其精确的病理诊断不仅关系到患者的治疗方案选择,也直接影响到治疗效果和患者生存质量。随着医学影像技术的不断进步,数字病理图像已逐渐成为临床诊断的标准手段,由此也带来对大量数据进行处理和分析的挑战。深度学习,尤其是卷积神经网络(convolutional neural networks,CNN)在自动化分析乳腺肿瘤病理图像方面展现了显著的优势和潜力,为提升诊断的精确度和效率开辟了新的途径。本综述旨在系统性地探讨深度学习,特别是CNN在乳腺肿瘤病理图像分类、检测识别和分割等方面的最新研究进展和应用。本文深入分析了该领域当前所面临的技术挑战,如数据稀缺性、模型可解释性以及模型泛化的问题,并对这些问题提出了可能的解决策略。最后,本文展望了未来的研究方向,特别关注于如何融合多模态数据、增强模型的鲁棒性和解释性等方面,以期为乳腺癌病理图像分析领域的未来研究提供有益的参考和启示。通过本综述,希望能够引起更多研究者的关注,推动该领域的研究进展,进一步促进深度学习技术在临床实践中的应用,为乳腺癌的早期诊断以及预后预测提供更为精准的决策依据。As one of the most prevalent malignant tumours in women,breast cancer poses a serious threat to women’s health worldwide.Its accurate pathological diagnosis not only relates to the choice of treatment plan for patients,but also directly affects the treatment effect and the quality of patients’survival.With the continuous progress of medical imaging technology,digital pathology images have gradually become the standard means of clinical diagnosis,which also brings the challenge of processing and analysing large amounts of data.Deep learning,especially convolutional neural networks(CNNs),has demonstrated significant advantages and potentials in automating the analysis of breast tumour pathology images,opening new avenues for improving the accuracy and efficiency of diagnosis.The aim of this review is to systematically explore the latest research advances and applications of deep learning,especially CNNs,in breast tumour pathology image classification,detection recognition and segmentation.This paper provides an in-depth analysis of the current technical challenges faced in this field,such as the problems of data scarcity,model interpretability,and model generalisation,and proposes possible solution strategies to these problems.Finally,this paper looks into future research directions,with special focus on how to fuse multimodal data,enhance model robustness and interpretability,with a view to providing useful references and insights for future research in the field of breast cancer pathology image analysis.Through this review,we hope to attract more researchers’attention,promote the research progress in this field,further promote the application of deep learning technology in clinical practice,and provide a more accurate decision basis for the early diagnosis of breast cancer as well as prognosis prediction.

关 键 词：乳腺肿瘤 深度学习 病理图像 辅助诊断 

分 类 号：R318.04[医药卫生—生物医学工程]