Towards domain-specific explainable AI: model interpretation of a skin image classifier using a human approach

  • Machine Learning models have started to outperform medical experts in some classification tasks. Meanwhile, the question of how these classifiers produce certain results is attracting increasing research attention. Current interpretation methods provide a good starting point in investigating such questions, but they still massively lack the relation to the problem domain. In this work, we present how explanations of an AI system for skin image analysis can be made more domain-specific. We apply the synthesis of Local Interpretable Model-agnostic Explanations (LIME) with the ABCD-rule, a diagnostic approach of dermatologists, and present the results using a Deep Neural Network (DNN) based skin image classifier.

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Author:Fabian StielerORCiDGND, Fabian RabeGND, Bernhard BauerORCiDGND
Frontdoor URL
Parent Title (English):Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2021, 19-25 June 2021, Nashville, TN, USA
Place of publication:Piscataway, NJ
Type:Conference Proceeding
Year of first Publication:2021
Publishing Institution:Universität Augsburg
Release Date:2021/06/22
First Page:1802
Last Page:1809
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Softwaretechnik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Softwaretechnik / Professur Softwaremethodik für verteilte Systeme
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):Deutsches Urheberrecht