Anomaly Detection for Skin Lesion Images Using Replicator Neural Networks

Fabrizio Nunnari, Hasan Md Tusfiqur Alam, Daniel Sonntag

In: Andreas Holzinger, Peter Kieseberg, A. Min Tjoa, Edgar Weippl (Hrsg.). Machine Learning and Knowledge Extraction. International IFIP Cross Domain (CD) Conference for Machine Learning & Knowledge Extraction (MAKE) (CD-MAKE-2021) August 17-20 Virtual Seiten 225-240 LNCS 12844 ISBN 978-3-030-84060-0 Springer International Publishing 2021.


This paper presents an investigation on the task of anomaly detection for images of skin lesions. The goal is to provide a decision support system with an extra filtering layer to inform users if a classifier should not be used for a given sample. We tested anomaly detectors based on autoencoders and three discrimination methods: feature vector distance, replicator neural networks, and support vector data description fine-tuning. Results show that neural-based detectors can perfectly discriminate between skin lesions and open world images, but class discrimination cannot easily be accomplished and requires further investigation.


Weitere Links

2021_CD_MAKE_AnomalyDetection.pdf (pdf, 4 MB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence