TY - JOUR AU - Brinker, Titus Josef AU - Hekler, Achim AU - Utikal, Jochen Sven AU - Grabe, Niels AU - Schadendorf, Dirk AU - Klode, Joachim AU - Berking, Carola AU - Steeb, Theresa AU - Enk, Alexander H AU - von Kalle, Christof PY - 2018 DA - 2018/10/17 TI -使用卷积神经网络进行皮肤癌分类:系统回顾JO - J医学Internet Res SP - e11936 VL - 20 IS - 10kw -皮肤癌KW -卷积神经网络KW -病变分类KW -深度学习KW -黑色素瘤分类KW -癌症分类AB -背景:研究显示，基于卷积神经网络(cnn)的最先进的分类器可以与皮肤科医生一样对皮肤癌图像进行分类，甚至可以通过在移动设备上安装应用程序在医院之外进行救生和快速诊断。据我们所知，目前还没有对这一研究领域的工作进行综述。目的:本研究首次系统综述了利用神经网络对皮肤损伤进行分类的最新研究。我们将我们的审查局限于皮肤病变分类。特别地，这里不考虑仅应用CNN进行分割或皮肤镜模式分类的方法。此外，本研究讨论了为什么所提出的程序的可比性是非常困难的，以及哪些挑战必须在未来解决。方法:我们检索了谷歌Scholar、PubMed、Medline、ScienceDirect和Web of Science数据库，以获得系统综述和英语发表的原创研究文章。只有报告了足够科学进展的论文才被纳入本综述。 Results: We found 13 papers that classified skin lesions using CNNs. In principle, classification methods can be differentiated according to three principles. Approaches that use a CNN already trained by means of another large dataset and then optimize its parameters to the classification of skin lesions are the most common ones used and they display the best performance with the currently available limited datasets. Conclusions: CNNs display a high performance as state-of-the-art skin lesion classifiers. Unfortunately, it is difficult to compare different classification methods because some approaches use nonpublic datasets for training and/or testing, thereby making reproducibility difficult. Future publications should use publicly available benchmarks and fully disclose methods used for training to allow comparability. SN - 1438-8871 UR - //www.mybigtv.com/2018/10/e11936/ UR - https://doi.org/10.2196/11936 UR - http://www.ncbi.nlm.nih.gov/pubmed/30333097 DO - 10.2196/11936 ID - info:doi/10.2196/11936 ER -