Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology: A three center study

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung


  • Christoph Wies - , Deutsches Krebsforschungszentrum (DKFZ) (Autor:in)
  • Lucas Schneider - , Deutsches Krebsforschungszentrum (DKFZ) (Autor:in)
  • Sarah Haggenmüller - , Deutsches Krebsforschungszentrum (DKFZ) (Autor:in)
  • Tabea-Clara Bucher - , Deutsches Krebsforschungszentrum (DKFZ) (Autor:in)
  • Sarah Hobelsberger - , Klinik und Poliklinik für Dermatologie, Hauttumorzentrum, Universitätsklinikum Carl Gustav Carus Dresden (Autor:in)
  • Markus V Heppt - , Friedrich-Alexander-Universität Erlangen-Nürnberg (Autor:in)
  • Gerardo Ferrara - , Anatomic Pathology and Cytopathology Unit-Istituto Nazionale Tumori di Napoli (Autor:in)
  • Eva I Krieghoff-Henning - , Deutsches Krebsforschungszentrum (DKFZ) (Autor:in)
  • Titus J Brinker - , Deutsches Krebsforschungszentrum (DKFZ) (Autor:in)


Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to improve their accuracy in diagnosing melanomas. The use of diagnostic Deep Learning (DL)-based support systems for automated examination of tissue morphology and cellular composition has been well studied in standard H&E-stained tissue slides. In contrast, there are few studies that analyze IHC slides using DL. Therefore, we investigated the separate and joint performance of ResNets trained on MelanA and corresponding H&E-stained slides. The MelanA classifier achieved an area under receiver operating characteristics curve (AUROC) of 0.82 and 0.74 on out of distribution (OOD)-datasets, similar to the H&E-based benchmark classification of 0.81 and 0.75, respectively. A combined classifier using MelanA and H&E achieved AUROCs of 0.85 and 0.81 on the OOD datasets. DL MelanA-based assistance systems show the same performance as the benchmark H&E classification and may be improved by multi stain classification to assist pathologists in their clinical routine.


Seiten (von - bis)e0297146
FachzeitschriftPloS one
PublikationsstatusVeröffentlicht - Jan. 2024

Externe IDs

PubMedCentral PMC10798511
Scopus 85182868794
ORCID /0000-0001-5703-324X/work/152543186
Mendeley 8d53e3c7-d7a4-37d8-b1c7-20c99d867dca



  • Humans, Melanoma/diagnosis, Immunohistochemistry, Deep Learning, MART-1 Antigen, ROC Curve