Semantic Segmentation of Histopathological Slides for the Classification of Cutaneous Lymphoma and Eczema
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Previously, researchers from Switzerland published a deep learning-aided diagnostic tool for predicting mycosis fungoides or eczema using whole-slide images with corresponding segmentation maps of the epidermis and spongiosis as input; on a dataset of 307 whole-slide images from 93 patients, the classification accuracy showed an almost negligible difference compared with a dummy classifier.
“Previously, researchers from Switzerland (Scheurer et al, 2020) published a DL-aided diagnostic tool for predicting MF or eczema using WSIs with corresponding segmentation maps of the epidermis and spongiosis as input. However, on a dataset of 307 WSIs from 93 patients, the classification accuracy showed an almost negligible difference compared with a dummy classifier.”
From Deep Learning–Based Classification of Early-Stage Mycosis Fungoides and Benign Inflammatory Dermatoses on H&E-Stained Whole-Slide Images: A Retrospective, Proof-of-Concept Study (2025) · cites this paper as Scheurer et al, 2020
Scheurer et al.'s DL-aided diagnostic tool for discriminating between eczema and MF based on 93 patients showed classification accuracy with almost negligible difference compared with a dummy classifier.
“To this aim, in 2020, Scheurer et al. published a DL-aided diagnostic tool for discriminating between eczema and MF based on a dataset of 93 patients; however, the classification accuracy showed an almost negligible difference compared with a dummy classifier [36].”
From Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives (2025) · cites this paper as [36]
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939 · see citing works
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Artificial Intelligence for Dermatopathology: Current Trends and the Road Ahead
Chen, S. B., & Novoa, R. A. (2022) · Seminars in Diagnostic Pathology · doi:10.1053/j.semdp.2022.01.003 · Cited by 37 · see citing works
Artificial intelligence in dermatopathology: Updates, strengths, and challenges
Cazzato, G., & Rongioletti, F. (2024) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2024.06.010 · Cited by 24 · see citing works
Doeleman, T., Brussee, S., Hondelink, L. M., Westerbeek, D. W. F., Sequeira, A. M., Valkema, P. A., Jansen, P. M., He, J., Vermeer, M. H., Quint, K. D., et al. (2025) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2024.07.036 · Cited by 7 · see citing works
Della Mura, M., Sorino, J., Colagrande, A., Daruish, M., Ingravallo, G., Massaro, A., Cazzato, G., Lupo, C., Casatta, N., Ribatti, D., et al. (2025) · Medical Sciences · doi:10.3390/medsci13020070 · Cited by 6 · see citing works
- Authors
- Scheurer, J., Ferrari, C., Berenguer Todo Bom, L., Beer, M., Kempf, W., & Haug, L.
- Affiliations
- ETH Zurich (CH)
- Corresponding author
- Scheurer, Jérémy
- Venue
- Communications in Computer and Information Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 26-42
- ISSN
- 1865-0929 (ISSN-L)
- 1865-0937
- OpenAlex
- W3040756447
- Cited by
- 18
- References
- 2
- Citations by year
- 2025: 3 · 2024: 6 · 2023: 6 · 2022: 2 · 2021: 1
- FWCI
- 0.86
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Communications in computer and information science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- mycosis fungoides, cutaneous T-cell lymphoma, atopic dermatitis, whole slide images, semantic segmentation, U-Net, EfficientNetB7, skin cancer diagnosis
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