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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, Pages 225-240, LNCS, Vol. 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.


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