Brinker Titus Josef, Hekler Achim, Utikal Jochen Sven, Grabe Niels, Schadendorf Dirk, Klode Joachim, Berking Carola, Steeb Theresa, Enk Alexander H, von Kalle Christof
National Center for Tumor Diseases, Department of Translational Oncology, German Cancer Research Center, Heidelberg, Germany.
Department of Dermatology, University Hospital Heidelberg, University of Heidelberg, Heidelberg, Germany.
J Med Internet Res. 2018 Oct 17;20(10):e11936. doi: 10.2196/11936.
State-of-the-art classifiers based on convolutional neural networks (CNNs) were shown to classify images of skin cancer on par with dermatologists and could enable lifesaving and fast diagnoses, even outside the hospital via installation of apps on mobile devices. To our knowledge, at present there is no review of the current work in this research area.
This study presents the first systematic review of the state-of-the-art research on classifying skin lesions with CNNs. We limit our review to skin lesion classifiers. In particular, methods that apply a CNN only for segmentation or for the classification of dermoscopic patterns are not considered here. Furthermore, this study discusses why the comparability of the presented procedures is very difficult and which challenges must be addressed in the future.
We searched the Google Scholar, PubMed, Medline, ScienceDirect, and Web of Science databases for systematic reviews and original research articles published in English. Only papers that reported sufficient scientific proceedings are included in this review.
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.
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.
基于卷积神经网络（CNN）的先进分类器已被证明在对皮肤癌图像进行分类方面与皮肤科医生不相上下，并且即使在医院外通过在移动设备上安装应用程序也能实现挽救生命的快速诊断。据我们所知，目前尚无对该研究领域当前工作的综述。
本研究首次对使用CNN对皮肤病变进行分类的先进研究进行系统综述。我们将综述局限于皮肤病变分类器。特别是，仅将CNN用于分割或用于皮肤镜模式分类的方法不在此考虑。此外，本研究讨论了为何所呈现程序的可比性非常困难以及未来必须应对哪些挑战。
我们在谷歌学术、PubMed、Medline、ScienceDirect和科学网数据库中搜索以英文发表的系统综述和原创研究文章。本综述仅纳入报告了充分科学过程的论文。
我们找到了13篇使用CNN对皮肤病变进行分类的论文。原则上，分类方法可根据三个原则进行区分。使用已通过另一个大型数据集训练的CNN，然后针对皮肤病变分类优化其参数的方法是最常用的方法，并且在当前可用的有限数据集上表现最佳。
作为先进的皮肤病变分类器，CNN表现出高性能。不幸的是，由于一些方法使用非公开数据集进行训练和/或测试，使得不同分类方法难以比较，从而难以实现可重复性。未来的出版物应使用公开可用的基准，并充分披露用于训练的方法，以确保可比性。
