Mohs micrographic surgery (MMS) is the gold standard technique for removing high risk nonmelanoma skin cancer however, intraoperative histopathological examination demands significant time, effort, and professionality. The objective of this study is to develop a deep learning model to detect basal cell carcinoma (BCC) and artifacts on Mohs slides. A total of 731 Mohs slides from 51 patients with BCCs were used in this study, with 91 containing tumor and 640 without tumor which was defined as non-tumor. The dataset was employed to train U-Net based models that segment tumor and non-tumor regions on the slides. The segmented patches were classified as tumor, or non-tumor to produce predictions for whole slide images (WSIs). For the segmentation phase, the deep learning model success was measured using a Dice score with 0.70 and 0.67 value, area under the curve (AUC) score with 0.98 and 0.96 for tumor and non-tumor, respectively. For the tumor classification, an AUC of 0.98 for patch-based detection, and AUC of 0.91 for slide-based detection was obtained on the test dataset. We present an AI system that can detect tumors and non-tumors in Mohs slides with high success. Deep learning can aid Mohs surgeons and dermatopathologists in making more accurate decisions.
View on arXiv@article{yilmaz2025_2504.05219,
title={ An ensemble deep learning approach to detect tumors on Mohs micrographic surgery slides },
author={ Abdurrahim Yilmaz and Serra Atilla Aydin and Deniz Temur and Furkan Yuceyalcin and Berkin Deniz Kahya and Rahmetullah Varol and Ozay Gokoz and Gulsum Gencoglan and Huseyin Uvet and Gonca Elcin },
journal={arXiv preprint arXiv:2504.05219},
year={ 2025 }
}