Hauser, Katja and Kurz, Alexander and Haggenmueller, Sarah and Maron, Roman C. and von Kalle, Christof and Utikal, Jochen S. and Meier, Friedegund and Hobelsberger, Sarah and Gellrich, Frank F. and Sergon, Mildred and Hauschild, Axel and French, Lars E. and Heinzerling, Lucie and Schlager, Justin G. and Ghoreschi, Kamran and Schlaak, Max and Hilke, Franz J. and Poch, Gabriela and Kutzner, Heinz and Berking, Carola and Heppt, Markus and Erdmann, Michael and Haferkamp, Sebastian and Schadendorf, Dirk and Sondermann, Wiebke and Goebeler, Matthias and Schilling, Bastian and Kather, Jakob N. and Froehling, Stefan and Lipka, Daniel B. and Hekler, Achim and Krieghoff-Henning, Eva and Brinker, Titus J. (2022) Explainable artificial intelligence in skin cancer recognition: A systematic review. EUROPEAN JOURNAL OF CANCER, 167. pp. 54-69. ISSN 0959-8049, 1879-0852
Full text not available from this repository. (Request a copy)Abstract
Background: Due to their ability to solve complex problems, deep neural networks (DNNs) are becoming increasingly popular in medical applications. However, decisionmaking 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 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 Methods: 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 isting 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 Conclusion: 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. 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | IMAGE CLASSIFICATION; LEVEL CLASSIFICATION; NEURAL-NETWORK; MELANOMA; DERMATOLOGISTS; DIAGNOSIS; SUPERIOR; Artificial intelligence; Man-machine systems; Systematic review |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Dermatologie und Venerologie |
| Depositing User: | Petra Gürster |
| Date Deposited: | 07 Sep 2023 12:16 |
| Last Modified: | 07 Sep 2023 12:16 |
| URI: | https://pred.uni-regensburg.de/id/eprint/58662 |
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