Authors:
Alina Burgert
1
;
Babette Dellen
1
;
Uwe Jaekel
1
and
Dietrich Paulus
2
Affiliations:
1
Faculty of Mathematics, Informatics, and Technology, University of Applied Sciences Koblenz, Joseph-Rovan-Allee 2, 53424 Remagen, Germany
;
2
Institute for Computational Visualistics, University Koblenz, Universitätsstraße 1, 56070 Koblenz, Germany
Keyword(s):
Anomaly Detection, Semi-Supervised Learning, Dermatology.
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.
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