Hauser Katja, Kurz Alexander, Haggenmüller Sarah, Maron Roman C, von Kalle Christof, Utikal Jochen S, Meier Friedegund, Hobelsberger Sarah, Gellrich Frank F, Sergon Mildred, Hauschild Axel, French Lars E, Heinzerling Lucie, Schlager Justin G, Ghoreschi Kamran, Schlaak Max, Hilke Franz J, Poch Gabriela, Kutzner Heinz, Berking Carola, Heppt Markus V, Erdmann Michael, Haferkamp Sebastian, Schadendorf Dirk, Sondermann Wiebke, Goebeler Matthias, Schilling Bastian, Kather Jakob N, Fröhling Stefan, Lipka Daniel B, Hekler Achim, Krieghoff-Henning Eva, Brinker Titus J
Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany.
Department of Clinical-Translational Sciences, Charité University Medicine and Berlin Institute of Health (BIH), Berlin, Germany.
Eur J Cancer. 2022 May;167:54-69. doi: 10.1016/j.ejca.2022.02.025. Epub 2022 Apr 5.
Due to their ability to solve complex problems, deep neural networks (DNNs) are becoming increasingly popular in medical applications. However, decision-making by such algorithms is essentially a black-box process that renders it difficult for physicians to judge whether the decisions are reliable. The use of explainable artificial intelligence (XAI) is often suggested as a solution to this problem. We investigate how XAI is used for skin cancer detection: how is it used during the development of new DNNs? What kinds of visualisations are commonly used? Are there systematic evaluations of XAI with dermatologists or dermatopathologists?
Google Scholar, PubMed, IEEE Explore, Science Direct and Scopus were searched for peer-reviewed studies published between January 2017 and October 2021 applying XAI to dermatological images: the search terms histopathological image, whole-slide image, clinical image, dermoscopic image, skin, dermatology, explainable, interpretable and XAI were used in various combinations. Only studies concerned with skin cancer were included.
37 publications fulfilled our inclusion criteria. Most studies (19/37) simply applied existing XAI methods to their classifier to interpret its decision-making. Some studies (4/37) proposed new XAI methods or improved upon existing techniques. 14/37 studies addressed specific questions such as bias detection and impact of XAI on man-machine-interactions. However, only three of them evaluated the performance and confidence of humans using CAD systems with XAI.
XAI is commonly applied during the development of DNNs for skin cancer detection. However, a systematic and rigorous evaluation of its usefulness in this scenario is lacking.
由于能够解决复杂问题，深度神经网络（DNN）在医学应用中越来越受欢迎。然而，此类算法的决策本质上是一个黑箱过程，这使得医生难以判断决策是否可靠。人们经常建议使用可解释人工智能（XAI）来解决这一问题。我们研究了XAI如何用于皮肤癌检测：在新DNN的开发过程中是如何使用的？常用哪些类型的可视化方法？是否有针对皮肤科医生或皮肤病理学家对XAI进行的系统评估？
在谷歌学术、PubMed、IEEE Xplore、科学Direct和Scopus数据库中搜索2017年1月至2021年10月发表的将XAI应用于皮肤病图像的同行评审研究：搜索词组织病理学图像、全切片图像、临床图像、皮肤镜图像、皮肤、皮肤病学、可解释的、可解释性和XAI以各种组合形式使用。仅纳入与皮肤癌相关的研究。
37篇出版物符合我们的纳入标准。大多数研究（19/37）只是将现有的XAI方法应用于其分类器以解释其决策过程。一些研究（4/37）提出了新的XAI方法或对现有技术进行了改进。14/37的研究解决了诸如偏差检测和XAI对人机交互的影响等具体问题。然而，其中只有三项研究评估了使用带有XAI的计算机辅助检测（CAD）系统的人员的性能和信心。
XAI在用于皮肤癌检测的DNN开发过程中普遍应用。然而，在这种情况下缺乏对其有用性的系统而严格的评估。
