计算机科学与探索2026，Vol.20Issue(1)：21-39,19.DOI:10.3778/j.issn.1673-9418.2504019
深度学习在皮肤病变图像分割中的研究综述
Research Review of Deep Learning in Skin Lesions Image Segmentation
摘要
Abstract
Skin lesions exhibit a wide variety of types and complex clinical manifestations,ranging from benign condi-tions to malignant melanomas.Early detection and accurate segmentation of these lesions are critical for the diagnosis and treatment of skin cancer,particularly in the early identification and localization of high-risk lesions such as malignant mel-anoma,which can significantly improve patient survival rates.In recent years,deep learning techniques have achieved remarkable progress in skin lesion image segmentation,greatly enhancing both accuracy and efficiency.This paper presents a comprehensive review of deep learning research in the field of skin lesion image segmentation.First,various skin lesion imaging modalities and commonly used public datasets are introduced,along with a summary of standard evaluation metrics.Then,addressing the prevalent issues of noise and artifacts in images,a detailed discussion on various image preprocessing and augmentation techniques is provided.Subsequently,deep learning-based segmentation methods are elaborated,covering U-Net,Transformer,SAM(segment anything model),Mamba,and multi-network fusion models.The main architectural designs,advantages,limitations,and segmentation performance of these models are comparatively analyzed.Finally,the current challenges and issues in this field are examined,and future research directions are proposed,aiming to provide valuable insights for the continued development of skin lesion image segmentation.关键词
皮肤病变分割/深度学习/医学图像/卷积神经网络Key words
skin lesions segmentation/deep learning/medical imaging/convolutional neural network分类
信息技术与安全科学引用本文复制引用
孟祥福,李佳讯,俞纯林,鲁蕴萱..深度学习在皮肤病变图像分割中的研究综述[J].计算机科学与探索,2026,20(1):21-39,19.基金项目
国家自然科学基金(61772249) （61772249）
辽宁省自然科学基金联合计划项目(20240311).This work was supported by the National Natural Science Foundation of China(61772249),and the Liaoning Provincial Natural Sci-ence Foundation Joint Program(20240311). （20240311）
