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| Verfasst von: | Maron, Roman C. [VerfasserIn]  |
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| | Haggenmüller, Sarah [VerfasserIn] |
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| | Kalle, Christof von [VerfasserIn] |
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| | Utikal, Jochen [VerfasserIn] |
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| | Meier, Friedegund [VerfasserIn] |
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| | Gellrich, Frank F. [VerfasserIn] |
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| | Hauschild, Axel [VerfasserIn] |
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| | French, Lars E. [VerfasserIn] |
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| | Schlaak, Max [VerfasserIn] |
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| | Ghoreschi, Kamran [VerfasserIn] |
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| | Kutzner, Heinz [VerfasserIn] |
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| | Heppt, Markus V. [VerfasserIn] |
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| | Haferkamp, Sebastian [VerfasserIn] |
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| | Sondermann, Wiebke [VerfasserIn] |
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| | Schadendorf, Dirk [VerfasserIn] |
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| | Schilling, Bastian [VerfasserIn] |
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| | Hekler, Achim [VerfasserIn] |
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| | Krieghoff-Henning, Eva [VerfasserIn] |
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| | Kather, Jakob Nikolas [VerfasserIn] |
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| | Fröhling, Stefan [VerfasserIn] |
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| | Lipka, Daniel [VerfasserIn] |
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| | Brinker, Titus Josef [VerfasserIn] |
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| Titel: | Robustness of convolutional neural networks in recognition of pigmented skin lesions |
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| Verf.angabe: | Roman C. Maron, Sarah Haggenmüller, Christof von Kalle, Jochen S. Utikal, Friedegund Meier, Frank F. Gellrich, Axel Hauschild, Lars E. French, Max Schlaak, Kamran Ghoreschi, Heinz Kutzner, Markus V. Heppt, Sebastian Haferkamp, Wiebke Sondermann, Dirk Schadendorf, Bastian Schilling, Achim Hekler, Eva Krieghoff-Henning, Jakob N. Kather, Stefan Fröhling, Daniel B. Lipka, Titus J. Brinker |
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| E-Jahr: | 2021 |
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| Jahr: | 7 January 2021 |
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| Umfang: | 11 S. |
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| Fussnoten: | Gesehen am 21.04.2021 |
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| Titel Quelle: | Enthalten in: European journal of cancer |
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| Ort Quelle: | Amsterdam [u.a.] : Elsevier, 1965 |
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| Jahr Quelle: | 2021 |
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| Band/Heft Quelle: | 145(2021), Seite 81-91 |
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| ISSN Quelle: | 1879-0852 |
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| Abstract: | Background - A basic requirement for artificial intelligence (AI)-based image analysis systems, which are to be integrated into clinical practice, is a high robustness. Minor changes in how those images are acquired, for example, during routine skin cancer screening, should not change the diagnosis of such assistance systems. - Objective - To quantify to what extent minor image perturbations affect the convolutional neural network (CNN)-mediated skin lesion classification and to evaluate three possible solutions for this problem (additional data augmentation, test-time augmentation, anti-aliasing). - Methods - We trained three commonly used CNN architectures to differentiate between dermoscopic melanoma and nevus images. Subsequently, their performance and susceptibility to minor changes (‘brittleness’) was tested on two distinct test sets with multiple images per lesion. For the first set, image changes, such as rotations or zooms, were generated artificially. The second set contained natural changes that stemmed from multiple photographs taken of the same lesions. - Results - All architectures exhibited brittleness on the artificial and natural test set. The three reviewed methods were able to decrease brittleness to varying degrees while still maintaining performance. The observed improvement was greater for the artificial than for the natural test set, where enhancements were minor. - Conclusions - Minor image changes, relatively inconspicuous for humans, can have an effect on the robustness of CNNs differentiating skin lesions. By the methods tested here, this effect can be reduced, but not fully eliminated. Thus, further research to sustain the performance of AI classifiers is needed to facilitate the translation of such systems into the clinic. |
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| DOI: | doi:10.1016/j.ejca.2020.11.020 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext: https://doi.org/10.1016/j.ejca.2020.11.020 |
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| | Volltext: https://www.sciencedirect.com/science/article/pii/S0959804920313575 |
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| | DOI: https://doi.org/10.1016/j.ejca.2020.11.020 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Artificial intelligence |
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| | Deep learning |
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| | Dermatology |
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| | Machine learning |
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| | Melanoma |
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| | Neural networks |
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| | Nevus |
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| | Skin neoplasms |
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| K10plus-PPN: | 1755523629 |
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| Verknüpfungen: | → Zeitschrift |
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Robustness of convolutional neural networks in recognition of pigmented skin lesions / Maron, Roman C. [VerfasserIn]; 7 January 2021 (Online-Ressource)
