Explainable artificial intelligence in skin cancer recognition: A systematic review
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....
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| Published in | European journal of cancer (1990) Vol. 167; pp. 54 - 69 |
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| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Elsevier Ltd
01.05.2022
Elsevier Science Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0959-8049 1879-0852 1879-0852 |
| DOI | 10.1016/j.ejca.2022.02.025 |
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| Abstract | 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?
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.
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.
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.
•No evaluation of explainable artificial intelligence (XAI) for skin cancer detection has been conducted to this date.•Overview of 37 studies using XAI on dermatological and dermato histological data.•Analysis of the usage of XAI to inform research on its role as part of computer-aided diagnosis (CAD) systems. |
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| AbstractList | 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?BACKGROUNDDue 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?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.METHODSGoogle 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.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.RESULTS37 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.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.CONCLUSIONXAI 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. 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. 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? 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. 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. 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. •No evaluation of explainable artificial intelligence (XAI) for skin cancer detection has been conducted to this date.•Overview of 37 studies using XAI on dermatological and dermato histological data.•Analysis of the usage of XAI to inform research on its role as part of computer-aided diagnosis (CAD) systems. 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? 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. 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. 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. |
| Author | Krieghoff-Henning, Eva Meier, Friedegund Berking, Carola Kather, Jakob N. Schlaak, Max Sergon, Mildred Sondermann, Wiebke Brinker, Titus J. Heinzerling, Lucie Haggenmüller, Sarah Ghoreschi, Kamran Goebeler, Matthias Gellrich, Frank F. Poch, Gabriela Heppt, Markus V. Hauschild, Axel Schlager, Justin G. Hilke, Franz J. Schadendorf, Dirk Hobelsberger, Sarah Kutzner, Heinz Erdmann, Michael Kurz, Alexander Maron, Roman C. Schilling, Bastian Fröhling, Stefan Lipka, Daniel B. French, Lars E. Hauser, Katja Hekler, Achim Haferkamp, Sebastian Utikal, Jochen S. von Kalle, Christof |
| Author_xml | – sequence: 1 givenname: Katja orcidid: 0000-0001-9390-3505 surname: Hauser fullname: Hauser, Katja organization: Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany – sequence: 2 givenname: Alexander surname: Kurz fullname: Kurz, Alexander organization: Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany – sequence: 3 givenname: Sarah surname: Haggenmüller fullname: Haggenmüller, Sarah organization: Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany – sequence: 4 givenname: Roman C. surname: Maron fullname: Maron, Roman C. organization: Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany – sequence: 5 givenname: Christof surname: von Kalle fullname: von Kalle, Christof organization: Department of Clinical-Translational Sciences, Charité University Medicine and Berlin Institute of Health (BIH), Berlin, Germany – sequence: 6 givenname: Jochen S. surname: Utikal fullname: Utikal, Jochen S. organization: Department of Dermatology, Heidelberg University, Mannheim, Germany – sequence: 7 givenname: Friedegund orcidid: 0000-0003-4340-9706 surname: Meier fullname: Meier, Friedegund organization: Skin Cancer Center at the University Cancer Centre and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany – sequence: 8 givenname: Sarah orcidid: 0000-0001-5703-324X surname: Hobelsberger fullname: Hobelsberger, Sarah organization: Skin Cancer Center at the University Cancer Centre and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany – sequence: 9 givenname: Frank F. orcidid: 0000-0002-2164-4644 surname: Gellrich fullname: Gellrich, Frank F. organization: Skin Cancer Center at the University Cancer Centre and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany – sequence: 10 givenname: Mildred orcidid: 0000-0003-4303-2101 surname: Sergon fullname: Sergon, Mildred organization: Skin Cancer Center at the University Cancer Centre and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany – sequence: 11 givenname: Axel orcidid: 0000-0002-1212-9587 surname: Hauschild fullname: Hauschild, Axel organization: Department of Dermatology, University Hospital (UKSH), Kiel, Germany – sequence: 12 givenname: Lars E. surname: French fullname: French, Lars E. organization: Department of Dermatology and Allergy, University Hospital, LMU Munich, Munich, Germany – sequence: 13 givenname: Lucie surname: Heinzerling fullname: Heinzerling, Lucie organization: Department of Dermatology and Allergy, University Hospital, LMU Munich, Munich, Germany – sequence: 14 givenname: Justin G. surname: Schlager fullname: Schlager, Justin G. organization: Department of Dermatology and Allergy, University Hospital, LMU Munich, Munich, Germany – sequence: 15 givenname: Kamran orcidid: 0000-0002-5526-7517 surname: Ghoreschi fullname: Ghoreschi, Kamran organization: Department of Dermatology, Venereology and Allergology, Charité – Universitätsmedizin Berlin, Berlin, Germany – sequence: 16 givenname: Max orcidid: 0000-0002-1663-8098 surname: Schlaak fullname: Schlaak, Max organization: Department of Dermatology, Venereology and Allergology, Charité – Universitätsmedizin Berlin, Berlin, Germany – sequence: 17 givenname: Franz J. orcidid: 0000-0002-6564-454X surname: Hilke fullname: Hilke, Franz J. organization: Department of Dermatology, Venereology and Allergology, Charité – Universitätsmedizin Berlin, Berlin, Germany – sequence: 18 givenname: Gabriela orcidid: 0000-0003-3948-2505 surname: Poch fullname: Poch, Gabriela organization: Department of Dermatology, Venereology and Allergology, Charité – Universitätsmedizin Berlin, Berlin, Germany – sequence: 19 givenname: Heinz orcidid: 0000-0001-6248-3050 surname: Kutzner fullname: Kutzner, Heinz organization: Dermatopathology Laboratory, Friedrichshafen, Germany – sequence: 20 givenname: Carola surname: Berking fullname: Berking, Carola organization: Department of Dermatology, University Hospital Erlangen, Comprehensive Cancer Center Erlangen - EMN, Friedrich-Alexander University Erlangen, Nuremberg, Germany – sequence: 21 givenname: Markus V. orcidid: 0000-0003-4603-1825 surname: Heppt fullname: Heppt, Markus V. organization: Department of Dermatology, University Hospital Erlangen, Comprehensive Cancer Center Erlangen - EMN, Friedrich-Alexander University Erlangen, Nuremberg, Germany – sequence: 22 givenname: Michael orcidid: 0000-0001-7136-6489 surname: Erdmann fullname: Erdmann, Michael organization: Department of Dermatology, University Hospital Erlangen, Comprehensive Cancer Center Erlangen - EMN, Friedrich-Alexander University Erlangen, Nuremberg, Germany – sequence: 23 givenname: Sebastian surname: Haferkamp fullname: Haferkamp, Sebastian organization: Department of Dermatology, University Hospital Regensburg, Regensburg, Germany – sequence: 24 givenname: Dirk surname: Schadendorf fullname: Schadendorf, Dirk organization: Department of Dermatology, University Hospital Essen, Essen, Germany – sequence: 25 givenname: Wiebke orcidid: 0000-0002-3684-3523 surname: Sondermann fullname: Sondermann, Wiebke organization: Department of Dermatology, University Hospital Essen, Essen, Germany – sequence: 26 givenname: Matthias orcidid: 0000-0001-7095-9848 surname: Goebeler fullname: Goebeler, Matthias organization: Department of Dermatology, University Hospital Würzburg, Würzburg, Germany – sequence: 27 givenname: Bastian orcidid: 0000-0001-8859-4103 surname: Schilling fullname: Schilling, Bastian organization: Department of Dermatology, University Hospital Würzburg, Würzburg, Germany – sequence: 28 givenname: Jakob N. orcidid: 0000-0002-3730-5348 surname: Kather fullname: Kather, Jakob N. organization: Division of Translational Medical Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany – sequence: 29 givenname: Stefan surname: Fröhling fullname: Fröhling, Stefan organization: National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany – sequence: 30 givenname: Daniel B. orcidid: 0000-0001-5081-7869 surname: Lipka fullname: Lipka, Daniel B. organization: National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany – sequence: 31 givenname: Achim surname: Hekler fullname: Hekler, Achim organization: Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany – sequence: 32 givenname: Eva orcidid: 0000-0001-8381-3100 surname: Krieghoff-Henning fullname: Krieghoff-Henning, Eva organization: Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany – sequence: 33 givenname: Titus J. orcidid: 0000-0002-3620-5919 surname: Brinker fullname: Brinker, Titus J. email: titus.brinker@dkfz.de organization: Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35390650$$D View this record in MEDLINE/PubMed |
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| Copyright | 2022 The Author(s) Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved. Copyright Elsevier Science Ltd. May 2022 |
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| Keywords | Systematic review Dermatology Skin neoplasms Artificial intelligence Man-machine systems |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved. |
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| Snippet | Due to their ability to solve complex problems, deep neural networks (DNNs) are becoming increasingly popular in medical applications. However, decision-making... Background: Due to their ability to solve complex problems, deep neural networks (DNNs) are becoming increasingly popular in medical applications. However,... |
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| SubjectTerms | Algorithms Artificial Intelligence Artificial neural networks Cancer Decision making Dermatology Explainable artificial intelligence Humans Man-machine systems Medical imaging Neural networks Neural Networks, Computer Performance evaluation Physicians Search engines Skin cancer Skin neoplasms Skin Neoplasms - diagnosis Systematic review |
| Title | Explainable artificial intelligence in skin cancer recognition: A systematic review |
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