# Detection of basal cell carcinoma on whole-slide images from Mohs micrographic surgery using weakly supervised learning

Article · Villiamsson K, Fornstedt L, Litjens G, Amir AL, Sjöblom N, Kaatonen A, Vesala O, Yacob F, Paoli J, Neittaanmäki N

- Archive id: doi:10.1016/j.jdin.2026.07.004
- Published: 2026-07-20
- Venue: JAAD international
- DOI: https://doi.org/10.1016/j.jdin.2026.07.004
- PMID: 42621943
- PMCID: PMC13487815
- Citations: 0
- Source URL: https://doi.org/10.1016/j.jdin.2026.07.004
- PDF: https://europepmc.org/articles/PMC13487815/pdf
- Provenance: europepmc

## Abstract

Background Mohs micrographic surgery (MMS) is the gold standard for treating aggressive basal cell carcinoma, but its success depends on expertise in intraoperative interpretation of frozen sections. Objective To develop weakly supervised, multiple instance learning framework using a pathology foundation model for automated basal cell carcinoma detection in MMS frozen sections. Methods An internal data set of 995 frozen MMS whole-slide images was slide-level labeled as tumor (512) or no tumor (483). Furthermore, tumor regions in the test set were annotated. Whole-slide images were tiled and encoded with Prov-Gigapath features for a weakly supervised multiple instance learning framework. The performance was evaluated as binary slide-level classification and in attention maps showing the localization of the tumor regions prior to validation on 2 external data sets. Results The model showed near-perfect diagnostic performance, achieving 97.0% accuracy, and an area under the receiver operating characteristic curve of 0.998 on the internal data set. Furthermore, attention maps visualized diagnostically relevant regions, enhancing model interpretability (Intersection-over-Union = 0.41 ± 0.046 \[Dice 0.58 ± 0.046\]). External validation confirmed robust performance (90% to 92% accuracy, area under the receiver operating characteristic curve: 0.92-0.95). Limitations The absence of tumor region annotations on external data sets. Conclusion These findings support the feasibility of artificial intelligence-assisted analysis of MMS frozen sections and justify prospective studies evaluating its integration into clinical workflows.

_Abstract reproduced under CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)._

Topics: [AI in Clinical Medicine](https://space-kitty.com/topics/ai-medicine)

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