摘要传统的机器学习受限于无法直接处理原始数据，而是依赖于专家设计特征提取器，但深度学习的出现打破了这一禁锢，可以自动地从未经处理的原始数据中发现用于检测或分类的代表性信息，成为人工智能医学影像分类的关键技术。在恶性黑素瘤与色素痣的二分类及黑素细胞来源肿瘤以外的其他皮肤疾病如鳞状细胞肿瘤、基底细胞癌、甲病等的分类方面，深度学习取得与皮肤科医师相当甚至超过皮肤科医师的分类水平。本文介绍深度学习在皮肤影像分类应用中的一些基本概念及常用的深度学习模型的评价方法，综述深度学习在皮肤影像分类中的研究进展。

abstractsConventional machine learning techniques can not be directly used to process natural data in their raw form, and have to rely on experts to design feature extractors. However, the emergence of deep learning has broken this limitation. It is a method that allows a machine to be fed with raw data and to automatically discover representative information needed for detection or classification, and has become a key technology for medical image classification with artificial intelligence. Deep learning has achieved a level comparable to or even higher than that of dermatologists in terms of classification between malignant melanoma and pigmented nevus, as well as classification between skin diseases other than melanocyte-derived tumors, such as squamous cell tumor, basal cell carcinoma and nail disorders. The review introduces some basic concepts of deep learning in skin image classification and common evaluation methods for deep learning models, and summarizes research progress in the application of deep learning in skin image classification.