Explainable artificial intelligence in skin cancer recognition: A systematic review

Hauser K, Kurz A, Haggenmüller S, Maron RC, von Kalle C, Utikal JS, Meier F, Hobelsberger S, Gellrich FF, Sergon M, Hauschild A, French LE, Heinzerling L, Schlager JG, Ghoreschi K, Schlaak M, Hilke FJ, Poch G, Kutzner H, Berking C, Heppt M, Erdmann M, Haferkamp S, Schadendorf D, Sondermann W, Goebeler M, Schilling B, Kather JN, Fröhling S, Lipka DB, Hekler A, Krieghoff-Henning E, Brinker TJ (2022)

Publication Type: Journal article, Review article

Publication year: 2022

Journal

European Journal of Cancer Elsevier

Book Volume: 167

Pages Range: 54-69

DOI: 10.1016/j.ejca.2022.02.025

Abstract

Background: 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? 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 in various combinations. Only studies concerned with skin cancer were included. Results: 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. 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.

Authors with CRIS profile

Carola Berking Lehrstuhl für Haut- und Geschlechtskrankheiten Markus Heppt Department of Dermatology

Involved external institutions

Universitätsklinikum Essen DE Germany (DE) Universitätsklinikum Würzburg DE Germany (DE) Klinikum der Universität München DE Germany (DE) Nationales Centrum für Tumorerkrankungen (NCT) Heidelberg DE Germany (DE) Berliner Institut für Gesundheitsforschung (BIH) DE Germany (DE) Deutsches Krebsforschungszentrum (DKFZ) DE Germany (DE) Universitätsklinikum Regensburg DE Germany (DE) Charité - Universitätsmedizin Berlin DE Germany (DE) Ruprecht-Karls-Universität Heidelberg DE Germany (DE) Universitätsklinikum Schleswig-Holstein (UKSH) DE Germany (DE)

How to cite

APA:

Hauser, K., Kurz, A., Haggenmüller, S., Maron, R.C., von Kalle, C., Utikal, J.S.,... Brinker, T.J. (2022). Explainable artificial intelligence in skin cancer recognition: A systematic review. European Journal of Cancer, 167, 54-69. https://doi.org/10.1016/j.ejca.2022.02.025

MLA:

Hauser, Katja, et al. "Explainable artificial intelligence in skin cancer recognition: A systematic review." European Journal of Cancer 167 (2022): 54-69.

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