红外与毫米波学报2025，Vol.44Issue(5)：680-691,12.DOI:10.11972/j.issn.1001-9014.2025.05.006
基于深度学习方法的OCT皮肤癌诊断:发展与展望
Deep learning based skin cancer diagnosis in OCT:progress and prospects
摘要
Abstract
Optical Coherence Tomography(OCT)provides high-resolution images of skin tissue structure and patholog-ical features.Automated image analysis methods(such as segmentation and classification)are important for assisting skin disease diagnosis and treatment evaluation.These methods provide quantitative support for medical decisions.Compared with traditional methods and early machine learning(ML)techniques,deep learning(DL)improved analy-sis efficiency and reproducibility.It also reduced manual processing time significantly.This paper systematically re-viewed the application progress of DL in skin OCT image analysis.It focused on technical approaches for image denois-ing,skin layer segmentation,and skin cancer diagnosis.The study identified key challenges including model general-ization and data heterogeneity.The findings provide theoretical references and technical guidance for future research di-rections.关键词
光学相干层析成像/深度学习/皮肤癌诊断/皮肤分层/图像降噪Key words
optical coherence tomography/deep learning/skin cancer diagnosis/skin segmentation/image denoising分类
计算机与自动化引用本文复制引用
张磊,李笑然,陈雯,雷梁锌雯,吴昊,卢忠,董必勤..基于深度学习方法的OCT皮肤癌诊断:发展与展望[J].红外与毫米波学报,2025,44(5):680-691,12.基金项目
上海市自然科学基金(22ZR1404300,22ZR1409500),上海市"科技创新行动计划"(22S31905500),复旦大学医工交叉项目(yg2021-032,yg2022-2),上海市"卫生健康青年人才"(2022YQ043),华山医院创新培育基金(2024CX06)Supported by the Natural Science Foundation of Shanghai(22ZR1404300,22ZR1409500) （22ZR1404300,22ZR1409500）
the Shanghai Science and Technology Innovation Action Plan(22S31905500) （22S31905500）
the Medical Engineering Fund of Fudan University(yg2021-032,yg2022-2) （yg2021-032,yg2022-2）
the Young Talents of Shanghai Health Commission(2022YQ043) （2022YQ043）
the Huashan Hospital Innovation Fund(2024CX06). （2024CX06）
