Deep learning for the detection of anatomical tissue structures and neoplasms of the skin on scanned histopathological tissue sections
Open accessAbstract
Basal cell carcinoma (BCC), squamous cell carcinoma (SqCC) and melanoma are among the most common cancer types. Correct diagnosis based on histological evaluation after biopsy or excision is paramount for adequate therapy stratification. Deep learning on histological slides has been suggested to complement and improve routine diagnostics, but publicly available curated and annotated data and usable models trained to distinguish common skin tumors are rare and often lack heterogeneous non-tumor categories. A total of 16 classes from 386 cases were manually annotated on scanned histological slides, 129,364 100 x 100 µm (~395 x 395 px) image tiles were extracted and split into a training, validation and test set. An EfficientV2 neuronal network was trained and optimized to classify image categories. Cross entropy loss, balanced accuracy and Matthews correlation coefficient were used for model evaluation. Image and patient data were assessed with confusion matrices. Application of the model to an external set of whole slides facilitated localization of melanoma and non-tumor tissue. Automated differentiation of BCC, SqCC, melanoma, naevi and non-tumor tissue structures was possible, and a high diagnostic accuracy was achieved in the validation (98%) and test (97%) set. In summary, we provide a curated dataset including the most common neoplasms of the skin and various anatomical compartments to enable researchers to train, validate and improve deep learning models. Automated classification of skin tumors by deep learning techniques is possible with high accuracy, facilitates tumor localization and has the potential to support and improve routine diagnostics.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The skin tumor patch classification task consists of tissue patches of 378 histopathology WSIs for classification of 16 categories.
“The skin tumor patch classification task66 consists of tissue patches of 378 histopathology WSIs from the archive of the Institute of Pathology, Heidelberg University, the MVZ for Histology, Cytology and Molecular Diagnostics Trier and the Institute for Dermatopathology Hannover for classification of 16 categories including 4 tumor types and 12 normal tissue structures.”
From A Multimodal Vision Foundation Model for Clinical Dermatology (2025) · cites this paper as [66]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
1 retracted
Li, T., Xie, P., Liu, J., Chen, M., Zhao, S., Kang, W., Zuo, K., & Li, F. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/5972962 · Cited by 21 · see citing works
RetractedSiegel, R. L., Miller, K. D., Fuchs, H. E., & Jemal, A. (2022) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21708 · Cited by 17,196 · see citing works
QuPath: Open source software for digital pathology image analysis
Bankhead, P., Loughrey, M. B., Fernández, J. A., Dombrowski, Y., McArt, D. G., Dunne, P. D., McQuaid, S., Gray, R. T., Murray, L. J., Coleman, H. G., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-17204-5 · Cited by 8,908 · see citing works
The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., et al. (2013) · Journal of Digital Imaging · doi:10.1007/s10278-013-9622-7 · Cited by 4,599 · see citing works
Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293 · see citing works
Randaugment: Practical automated data augmentation with a reduced search space
Cubuk, E. D., Zoph, B., Shlens, J., & Le, Q. V. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00359 · Cited by 3,223 · see citing works
Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0177-5 · Cited by 2,930 · see citing works
Relatlimab and Nivolumab versus Nivolumab in Untreated Advanced Melanoma
Tawbi, H. A., Schadendorf, D., Lipson, E. J., Ascierto, P. A., Matamala, L., Gutiérrez, E. C., Rutkowski, P., Gogas, H. J., Lao, C. D., Menezes, J. J. D., et al. (2022) · New England Journal of Medicine · doi:10.1056/nejmoa2109970 · Cited by 1,935 · see citing works
Prevalence and Costs of Skin Cancer Treatment in the U.S., 2002−2006 and 2007−2011
Guy, G. P., Machlin, S. R., Ekwueme, D. U., & Yabroff, K. R. (2014) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2014.08.036 · Cited by 871 · see citing works
A deep learning system for differential diagnosis of skin diseases
Liu, Y., Jain, A., Eng, C., Way, D. H., Lee, K., Bui, P., Kanada, K., de Oliveira Marinho, G., Gallegos, J., Gabriele, S., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0842-3 · Cited by 853 · see citing works
Bulten, W., Pinckaers, H., van Boven, H., Vink, R., de Bel, T., van Ginneken, B., van der Laak, J., Hulsbergen-van de Kaa, C., & Litjens, G. (2020) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30739-9 · Cited by 705 · see citing works
AI-based pathology predicts origins for cancers of unknown primary
Lu, M. Y., Chen, T. Y., Williamson, D. F. K., Zhao, M., Shady, M., Lipkova, J., & Mahmood, F. (2021) · Nature · doi:10.1038/s41586-021-03512-4 · Cited by 636 · see citing works
Prevalence of a History of Skin Cancer in 2007
Stern, R. S. (2010) · Archives of Dermatology · doi:10.1001/archdermatol.2010.4 · Cited by 421 · see citing works
WHO classification of skin tumours
Elder, D. E., Massi, D., Scolyer, R. A., et al. (2018) · Medical Entomology and Zoology · Cited by 420 · see citing works
Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification
Mahbod, A., Schaefer, G., Wang, C., Dorffner, G., Ecker, R., & Ellinger, I. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105475 · Cited by 320 · see citing works
AI-based pathology predicts origins for cancers of unknown primary
Lu, M. Y., Chen, T., Williamson, D. F. K., Zhao, M. M., Shady, M., Lipková, J., & Mahmood, F. (2021) · RePEc: Research Papers in Economics · Cited by 263 · see citing works
Chen, H., Li, C., Wang, G., Li, X., Rahaman, M. M., Sun, H., Hu, W., Li, Y., Liu, W., Sun, C., et al. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108827 · Cited by 245 · see citing works
Chen, C., Chen, C., Yu, W.-H., Chen, S. S., Chang, Y., Hsu, T., Hsiao, M., Yeh, C., & Chen, C. (2021) · Nature Communications · doi:10.1038/s41467-021-21467-y · Cited by 221 · see citing works
Taylor-Weiner, A. N., Pokkalla, H., Han, L., Jia, C., Huss, R. S., Chung, C., Elliott, H. L., Glass, B., Pethia, K., Carrasco‐Zevallos, O. M., et al. (2021) · Hepatology · doi:10.1002/hep.31750 · Cited by 194 · see citing works
Matek, C., Krappe, S., Münzenmayer, C., Haferlach, T., & Marr, C. (2021) · Blood · doi:10.1182/blood.2020010568 · Cited by 193 · see citing works
Deep Learning for the Classification of Small-Cell and Non-Small-Cell Lung Cancer
Kriegsmann, M., Haag, C., Weis, C., Steinbuß, G., Warth, A., Zgorzelski, C., Muley, T., Winter, H., Eichhorn, M., Eichhorn, F., et al. (2020) · Cancers · doi:10.3390/cancers12061604 · Cited by 136 · see citing works
Self-citationNumber of pathologists in Germany: comparison with European countries, USA, and Canada
Märkl, B., Füzesi, L., Huss, R., Bauer, S., & Schaller, T. (2021) · Virchows Archiv · doi:10.1007/s00428-020-02894-6 · Cited by 115 · see citing works
Deep learning enables automated scoring of liver fibrosis stages
Yu, Y., Wang, J., Ng, C. W., Ma, Y., Mo, S., Fong, E. L. S., Xing, J., Song, Z., Xie, Y., Si, K., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-34300-2 · Cited by 114 · see citing works
Ianni, J. D., Soans, R. E., Sankarapandian, S., Chamarthi, R. V., Ayyagari, D., Olsen, T. G., Bonham, M. J., Stavish, C. C., Motaparthi, K., Cockerell, C. J., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-59985-2 · Cited by 113 · see citing works
Echle, A., Ghaffari Laleh, N., Quirke, P., Grabsch, H. I., Muti, H. S., Saldanha, O. L., Brockmoeller, S. F., van den Brandt, P. A., Hutchins, G. G. A., Richman, S. D., et al. (2022) · ESMO Open · doi:10.1016/j.esmoop.2022.100400 · Cited by 111 · see citing works
A small proportion of Talin molecules transmit forces at developing muscle attachments in vivo
Lemke, S. B., Weidemann, T., Cost, A., Grashoff, C., & Schnorrer, F. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000057 · Cited by 89 · see citing works
Logu, F. D., Ugolini, F., Maio, V., Simi, S., Cossu, A., Massi, D., Nassini, R., & Laurino, M. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.01559 · Cited by 64 · see citing works
Kriegsmann, M., Kriegsmann, K., Steinbuß, G., Zgorzelski, C., Kraft, A., & Gaida, M. M. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22105385 · Cited by 42 · see citing works
Self-citationDiagnostic assessment of deep learning for melanocytic lesions using whole-slide pathological images
Ba, W., Wang, R., Yin, G., Song, Z., Zou, J., Zhong, C., Yang, J., Yu, G., Yang, H., Zhang, L., et al. (2021) · Translational Oncology · doi:10.1016/j.tranon.2021.101161 · Cited by 38 · see citing works
Non-melanoma skin cancer segmentation for histopathology dataset
Thomas, S. M., Lefevre, J., Baxter, G., et al. (2021) · Data in Brief · doi:10.1016/j.dib.2021.107587 · Cited by 29 · see citing works
Steinbuß, G., Kriegsmann, K., & Kriegsmann, M. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21186652 · Cited by 26 · see citing works
Self-citationEfficientNetv2 Model for Breast Cancer Histopathological Image Classification
Liu, D., Wang, W., Wu, X., et al. (2022) · International Conference on Electronic Communication and Artificial Intelligence (ICECAI) · doi:10.1109/iwecai55315.2022.00081 · Cited by 22 · see citing works
Common Pitfalls and Recommendations for Grand Challenges in Medical Artificial Intelligence
Reinke, A., Tizabi, M. D., Eisenmann, M., & Maier‐Hein, L. (2021) · European Urology Focus · doi:10.1016/j.euf.2021.05.008 · Cited by 21 · see citing works
Yeh, I. (2022) · Seminars in Diagnostic Pathology · doi:10.1053/j.semdp.2022.02.001 · Cited by 9 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [36]Achi HE Belousova T Chen L Wahed A Wang I Hu Z. Automated diagnosis of lymphoma with digital pathology images using deep learning. Ann Clin Lab Sci (2019) 49: 153–60. 31028058
Cited by
Sorted by citations
A Vision-Language Foundation Model for Precision Oncology
Xiang, J., Wang, X., Zhang, X., Xi, Y., Eweje, F., Chen, Y., Li, Y., Bergstrom, C., Gopaulchan, M., Kim, T., et al. (2025) · Nature · doi:10.1038/s41586-024-08378-w · Cited by 256 · see citing works
A Multimodal Vision Foundation Model for Clinical Dermatology
Yan, S., Yu, Z., Primiero, C., Vico-Alonso, C., Wang, Z., Yang, L., Tschandl, P., Hu, M., Ju, L., Tan, G., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03747-y · Cited by 117 · see citing works
“The UAH89k dataset 66 includes 269 histopathology whole slide images from Heidelberg University, MVZ for Histology, Cytology and Molecular Diagnostics Trier, and the Institute for Dermatopathol-ogy, enriching the model’s understanding of skin conditions at the microscopic level.”
- Authors
- Kriegsmann, K., Lobers, F., Zgorzelski, C., Kriegsmann, J., Janßen, C., Meliß, R. R., Muley, T., Sack, U., Steinbuss, G., & Kriegsmann, M.
- Affiliations
- Heidelberg University (DE)
- Leipzig University (DE)
- University of Bremen (DE)
- German Center for Lung Research (DE)
- Corresponding author
- Kriegsmann, Mark
- ORCID
- Venue
- Frontiers in Oncology
- Publisher
- Frontiers Media SA
- Published
- 2022-11-22
- Volume
- 12
- Pages
- 1022967-1022967
- ISSN
- 2234-943X (ISSN-L)
- PMC
- PMC9723465
- OpenAlex
- W4311027007
- Cited by
- 50
- References
- 34
- Citations by year
- 2026: 14 · 2025: 25 · 2024: 7 · 2023: 4
- FWCI
- 2.96
- Type
- journal article
- Language
- en
- Update notices
- corrigendum (2023-05-18)
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Oncology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Qucosa (Saxon State and University Library Dresden) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Medizinischen Fakultät Heidelberg, Universität Heidelberg
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- skin cancer, histopathology, melanoma, basal cell carcinoma, squamous cell carcinoma, nevi, whole slide images, histopathological image classification, EfficientNetV2, computer-aided diagnosis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
