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Focus of the project is to combine deep learning black box approaches with interpretable machine learning for classification of different types of medical images to combine the predictive accuracy of deep learning and the transparency and comprehensibility of interpretable models. Specifically, we present an extension of the Inductive Logic Programming system Aleph to allow for interactive learning. Medical experts can ask for verbal explanations. They can correct classification decisions and in addition can also correct the explanations. Thereby, expert knowledge can be taken into account in form of constraints for model adaption.<\/jats:p>","DOI":"10.1007\/s13218-020-00633-2","type":"journal-article","created":{"date-parts":[[2020,1,10]],"date-time":"2020-01-10T17:02:55Z","timestamp":1578675775000},"page":"227-233","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Mutual Explanations for Cooperative Decision Making in Medicine"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1301-0326","authenticated-orcid":false,"given":"Ute","family":"Schmid","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9415-6254","authenticated-orcid":false,"given":"Bettina","family":"Finzel","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,1,10]]},"reference":[{"key":"633_CR1","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","volume":"6","author":"A Adadi","year":"2018","unstructured":"Adadi A, Berrada M (2018) Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). 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