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Verfasst von:Höhn, Julia [VerfasserIn]   i
 Hekler, Achim [VerfasserIn]   i
 Krieghoff-Henning, Eva [VerfasserIn]   i
 Kather, Jakob Nikolas [VerfasserIn]   i
 Utikal, Jochen [VerfasserIn]   i
 Meier, Friedegund [VerfasserIn]   i
 Gellrich, Frank Friedrich [VerfasserIn]   i
 Hauschild, Axel [VerfasserIn]   i
 French, Lars E. [VerfasserIn]   i
 Schlager, Justin Gabriel [VerfasserIn]   i
 Ghoreschi, Kamran [VerfasserIn]   i
 Wilhelm, Tabea [VerfasserIn]   i
 Kutzner, Heinz [VerfasserIn]   i
 Heppt, Markus V. [VerfasserIn]   i
 Haferkamp, Sebastian [VerfasserIn]   i
 Sondermann, Wiebke [VerfasserIn]   i
 Schadendorf, Dirk [VerfasserIn]   i
 Schilling, Bastian [VerfasserIn]   i
 Maron, Roman C. [VerfasserIn]   i
 Schmitt, Max [VerfasserIn]   i
 Jutzi, Tanja [VerfasserIn]   i
 Fröhling, Stefan [VerfasserIn]   i
 Lipka, Daniel [VerfasserIn]   i
 Brinker, Titus Josef [VerfasserIn]   i
Titel:Integrating patient data into skin cancer classification using convolutional neural networks
Titelzusatz:systematic review
Verf.angabe:Julia Höhn, MSc; Achim Hekler, MSc; Eva Krieghoff-Henning, PhD; Jakob Nikolas Kather, Prof Dr med; Jochen Sven Utikal, Prof Dr med; Friedegund Meier, Univ Prof Dr med; Frank Friedrich Gellrich, Axel Hauschild, Dr med; Lars French, Dr med; Justin Gabriel Schlager, Dr med; Kamran Ghoreschi, Dr med; Tabea Wilhelm, Dr med; Heinz Kutzner, Dr med; Markus Heppt, PD Dr med; Sebastian Haferkamp, PD Dr med; Wiebke Sondermann, Dr med; Dirk Schadendorf, Dr med; Bastian Schilling, Dr med; Roman C. Maron, MSc; Max Schmitt, MSc; Tanja Jutzi, PhD; Stefan Fröhling, Prof Dr med; Daniel B. Lipka, PD Dr med; Titus Josef Brinker, Dr med, MD
E-Jahr:2021
Jahr:2.7.2021
Umfang:14 S.
Fussnoten:Gesehen am 12.10.2021
Titel Quelle:Enthalten in: Journal of medical internet research
Ort Quelle:Richmond, Va. : Healthcare World, 1999
Jahr Quelle:2021
Band/Heft Quelle:23(2021), 7, Artikel-ID e20708, Seite 1-14
ISSN Quelle:1438-8871
Abstract:Background: Recent years have been witnessing a substantial improvement in the accuracy of skin cancer classification using convolutional neural networks (CNNs). CNNs perform on par with or better than dermatologists with respect to the classification tasks of single images. However, in clinical practice, dermatologists also use other patient data beyond the visual aspects present in a digitized image, further increasing their diagnostic accuracy. Several pilot studies have recently investigated the effects of integrating different subtypes of patient data into CNN-based skin cancer classifiers. - Objective: This systematic review focuses on the current research investigating the impact of merging information from image features and patient data on the performance of CNN-based skin cancer image classification. This study aims to explore the potential in this field of research by evaluating the types of patient data used, the ways in which the nonimage data are encoded and merged with the image features, and the impact of the integration on the classifier performance. - Methods: Google Scholar, PubMed, MEDLINE, and ScienceDirect were screened for peer-reviewed studies published in English that dealt with the integration of patient data within a CNN-based skin cancer classification. The search terms skin cancer classification, convolutional neural network(s), deep learning, lesions, melanoma, metadata, clinical information, and patient data were combined. - Results: A total of 11 publications fulfilled the inclusion criteria. All of them reported an overall improvement in different skin lesion classification tasks with patient data integration. The most commonly used patient data were age, sex, and lesion location. The patient data were mostly one-hot encoded. There were differences in the complexity that the encoded patient data were processed with regarding deep learning methods before and after fusing them with the image features for a combined classifier. - Conclusions: This study indicates the potential benefits of integrating patient data into CNN-based diagnostic algorithms. However, how exactly the individual patient data enhance classification performance, especially in the case of multiclass classification problems, is still unclear. Moreover, a substantial fraction of patient data used by dermatologists remains to be analyzed in the context of CNN-based skin cancer classification. Further exploratory analyses in this promising field may optimize patient data integration into CNN-based skin cancer diagnostics for patients’ benefits.
DOI:doi:10.2196/20708
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/10.2196/20708
 Volltext: https://www.jmir.org/2021/7/e20708
 DOI: https://doi.org/10.2196/20708
Datenträger:Online-Ressource
Sprache:eng
K10plus-PPN:1773371185
Verknüpfungen:→ Zeitschrift

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