Explainable artificial intelligence in skin cancer recognition: A systematic review
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Le résumé fourni par la source
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.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Explainable artificial intelligence in skin cancer recognition: A systematic review
- Date Crossref
- 01/05/2022
- Éditeur
- Elsevier BV
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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German Cancer Research Center National Center for Tumor Diseases (NCT) pays non établi dans la noticeStructure de recherche
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Heidelberg University Department of Dermatology pays non établi dans la noticeUniversité ou école supérieure
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National Center for Tumor Diseases pays non établi dans la noticeOrganisme public
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Berlin Institute of Health at Charité - Universitätsmedizin Berlin pays non établi dans la noticeStructure de recherche
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University Hospital Carl Gustav Carus Department of Dermatology and Allergy pays non établi dans la noticeÉtablissement de santé
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Nationales Centrum für Tumorerkrankungen Dresden pays non établi dans la noticeÉtablissement de santé
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Technische Universität Dresden pays non établi dans la noticeUniversité ou école supérieure
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University Hospital Schleswig-Holstein pays non établi dans la noticeÉtablissement de santé
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University of Lübeck pays non établi dans la noticeUniversité ou école supérieure
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University of Miami Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery pays non établi dans la noticeUniversité ou école supérieure
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LMU Klinikum pays non établi dans la noticeÉtablissement de santé
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Ludwig-Maximilians-Universität München pays non établi dans la noticeUniversité ou école supérieure
National Center for Tumor Diseases (NCT) — German Cancer Research Center, Department of Dermatology — Heidelberg University et National Center for Tumor Diseases, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.