Effects of Label Noise on Deep Learning-Based Skin Cancer Classification
Open accessAbstract
Recent studies have shown that deep learning is capable of classifying dermatoscopic images at least as well as dermatologists. However, many studies in skin cancer classification utilize non-biopsy-verified training images. This imperfect ground truth introduces a systematic error, but the effects on classifier performance are currently unknown. Here, we systematically examine the effects of label noise by training and evaluating convolutional neural networks (CNN) with 804 images of melanoma and nevi labeled either by dermatologists or by biopsy. The CNNs are evaluated on a test set of 384 images by means of 4-fold cross validation comparing the outputs with either the corresponding dermatological or the biopsy-verified diagnosis. With identical ground truths of training and test labels, high accuracies with 75.03% (95% CI: 74.39-75.66%) for dermatological and 73.80% (95% CI: 73.10-74.51%) for biopsy-verified labels can be achieved. However, if the CNN is trained and tested with different ground truths, accuracy drops significantly to 64.53% (95% CI: 63.12-65.94%, p<0.01) on a non-biopsy-verified and to 64.24% (95% CI: 62.66-65.83%, p<0.01) on a biopsy-verified test set. In conclusion, deep learning methods for skin cancer classification are highly sensitive to label noise and future work should use biopsy-verified training images to mitigate this problem.
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Label noise can also derive from human uncertainty when using the consensus of multiple annotators, often needed due to the absence of a pathologically confirmed diagnosis label.
“Label noise can also derive from human uncertainty when using the consensus of multiple annotators, often needed due to the absence of a pathologically confirmed diagnosis label, thereby introducing inherent uncertainty35–37.”
From A scoping review of robustness concepts for machine learning in healthcare (2025) · cites this paper as [37]
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- Authors
- Hekler, A., Kather, J. N., Krieghoff-Henning, E., Utikal, J. S., Meier, F., Gellrich, F. F., Upmeier zu Belzen, J., French, L., Schlager, J. G., Ghoreschi, K., Wilhelm, T., Kutzner, H., Berking, C., Heppt, M. V., Haferkamp, S., Sondermann, W., Schadendorf, D., Schilling, B., Izar, B., Maron, R., Schmitt, M., Fröhling, S., Lipka, D. B., & Brinker, T. J.
- Affiliations
- German Cancer Research Center (DE)
- National Center for Tumor Diseases (DE)
- Ludwig-Maximilians-Universität München (DE)
- Charité – Universitätsmedizin Berlin (DE)
- Dermatopathologie Friedrichshafen (DE)
- Universitätsklinikum Erlangen (DE)
- University Hospital Regensburg (DE)
- Essen University Hospital (DE)
- Universitätsklinikum Würzburg (DE)
- Dana-Farber Cancer Institute (US)
- Corresponding author
- Brinker, Titus J.
- ORCID
- Hekler, Achim
- Kather, Jakob N.
- Utikal, Jochen S.
- Meier, Friedegund
- Gellrich, Frank F.
- French, Lars
- Schlager, Justin G.
- Ghoreschi, Kamran
- Wilhelm, Tabea
- Kutzner, Heinz
- Berking, Carola
- Heppt, Markus V.
- Haferkamp, Sebastian
- Sondermann, Wiebke
- Schadendorf, Dirk
- Schilling, Bastian
- Izar, Benjamin
- Maron, Roman
- Schmitt, Max
- Fröhling, Stefan
- Lipka, Daniel B.
- Brinker, Titus J.
- Venue
- Frontiers in Medicine
- Publisher
- Frontiers Media SA
- Published
- 2020-05-06
- Volume
- 7
- Pages
- 177-177
- ISSN
- 2296-858X (ISSN-L)
- PMC
- PMC7218064
- OpenAlex
- W3021577186
- Cited by
- 58
- References
- 16
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 12 · 2023: 10 · 2022: 19 · 2021: 9
- FWCI
- 3.43
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion, cc-by)
- University of Regensburg Publication Server (University of Regensburg) (submittedVersion)
- Online Publication Service of Würzburg University (Würzburg University) (submittedVersion, cc-by)
- OPUS FAU (Kooperativer Bibliotheksverbund Berlin-Brandenburg (KOBV), on behalf of the Universitätsbibliothek Erlangen-Nürnberg) (submittedVersion, cc-by)
- Funders
- Deutsches Krebsforschungszentrum
- Bundesministerium für Gesundheit
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- label noise, skin cancer classification, melanoma, nevi, dermoscopic images, convolutional neural networks, classification accuracy
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