This paper is in the following e-collection/theme issue:

Artificial Intelligence (1371) Imaging Informatics (193) Machine Learning (1465) Skin Cancer and Melanoma Prevention (112) Digital Photography and Imaging in Dermatology (32)

Published on in Vol 23, No 3 (2021): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/21695, first published .
Reducing the Impact of Confounding Factors on Skin Cancer Classification via Image Segmentation: Technical Model Study

Reducing the Impact of Confounding Factors on Skin Cancer Classification via Image Segmentation: Technical Model Study

Reducing the Impact of Confounding Factors on Skin Cancer Classification via Image Segmentation: Technical Model Study

Authors of this article:

Roman C Maron1 Author Orcid Image ;   Achim Hekler1 Author Orcid Image ;   Eva Krieghoff-Henning1 Author Orcid Image ;   Max Schmitt1 Author Orcid Image ;   Justin G Schlager2 Author Orcid Image ;   Jochen S Utikal3, 4 Author Orcid Image ;   Titus J Brinker1 Author Orcid Image
Article Authors Cited by (15) Tweetations (4) Metrics

Roman C Maron   1 , MSc ;   Achim Hekler   1 , MSc ;   Eva Krieghoff-Henning   1 , PhD ;   Max Schmitt   1 , MSc ;   Justin G Schlager   2 , MD ;   Jochen S Utikal   3, 4 , MD ;   Titus J Brinker   1 , MD

1 Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany

2 Department of Dermatology and Allergy, University Hospital, LMU Munich, Munich, Germany

3 Department of Dermatology, Heidelberg University, Mannheim, Germany

4 Skin Cancer Unit, German Cancer Research Center (DKFZ), Heidelberg, Germany

Corresponding Author:

  • Titus J Brinker, MD
  • Digital Biomarkers for Oncology Group
  • National Center for Tumor Diseases (NCT)
  • German Cancer Research Center (DKFZ)
  • Im Neuenheimer Feld 280
  • Heidelberg, 69120
  • Germany
  • Phone: 49 6221 3219304
  • Email: titus.brinker@dkfz.de