Semi-Supervised Anomaly Detection in Skin Lesion Images

Alina Burgert, Babette Dellen, Uwe Jaekel, Dietrich Paulus

2025

Abstract

Semi-supervised anomaly detection is the task of learning the pattern of normal samples and identifying deviations from this pattern as anomalies. This approach is especially helpful in the medical domain, since healthy samples are usually easy to collect and time-intensive annotation of training data is not necessary. In dermatology the utilization of this approach is not fully explored yet, since most work is limited to cancer detection, with the normal samples being nevi. This study, instead, investigates the use of semi-supervised anomaly detection methods for skin disease detection and localization. Due to the absence of a benchmark dataset a custom dataset was created. Based on this dataset two different models, SimpleNet and an autoencoder, were trained on healthy skin images only. Our experiment shows that both models are able to distinguish between normal and abnormal samples of the test dataset, with SimpleNet achieving an AUROC score of 97 % and the autoencoder a score of 93 %, demonstrating the potential of anomaly detection for dermatological applications. A visual analysis of corresponding anomaly maps revealed that both models have their own strengths and weaknesses when localizing the abnormal regions.

Paper Citation

in Harvard Style

Burgert A., Dellen B., Jaekel U. and Paulus D. (2025). Semi-Supervised Anomaly Detection in Skin Lesion Images. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 535-541. DOI: 10.5220/0013305400003912

in Bibtex Style

@conference{visapp25,
author={Alina Burgert and Babette Dellen and Uwe Jaekel and Dietrich Paulus},
title={Semi-Supervised Anomaly Detection in Skin Lesion Images},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2025},
pages={535-541},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013305400003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Semi-Supervised Anomaly Detection in Skin Lesion Images
SN - 978-989-758-728-3
AU - Burgert A.
AU - Dellen B.
AU - Jaekel U.
AU - Paulus D.
PY - 2025
SP - 535
EP - 541
DO - 10.5220/0013305400003912
PB - SciTePress