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Even though state-of-the-art deep learning models have achieved human-level accuracy on the classification of different types of medical data, these models are hardly adopted in clinical workflows, mainly due to their lack of interpretability. The black-box nature of deep learning models has raised the need for devising strategies to explain the decision process of these models, leading to the creation of the topic of eXplainable Artificial Intelligence (XAI). In this context, we provide a thorough survey of XAI applied to medical imaging diagnosis, including visual, textual, example-based and concept-based explanation methods. Moreover, this work reviews the existing medical imaging datasets and the existing metrics for evaluating the quality of the explanations. In addition, we include a performance comparison among a set of report generation\u2013based methods. Finally, the major challenges in applying XAI to medical imaging and the future research directions on the topic are discussed.<\/jats:p>","DOI":"10.1145\/3625287","type":"journal-article","created":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T08:00:10Z","timestamp":1695715210000},"page":"1-41","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":105,"title":["Explainable Deep Learning Methods in Medical Image Classification: A Survey"],"prefix":"10.1145","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2215-3334","authenticated-orcid":false,"given":"Cristiano","family":"Patr\u00edcio","sequence":"first","affiliation":[{"name":"University of Beira Interior and NOVA LINCS, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0139-2213","authenticated-orcid":false,"given":"Jo\u00e3o C.","family":"Neves","sequence":"additional","affiliation":[{"name":"University of Beira Interior and NOVA LINCS, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4050-7880","authenticated-orcid":false,"given":"Lu\u00eds F.","family":"Teixeira","sequence":"additional","affiliation":[{"name":"University of Porto and INESC TEC, Portugal"}]}],"member":"320","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.120"},{"key":"e_1_3_3_3_2","article-title":"Sanity checks for saliency maps","volume":"31","author":"Adebayo Julius","year":"2018","unstructured":"Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim. 2018. 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