Abstract
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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This research is partly funded by the pAItient project (BMG) and the Endowed Chair of Applied Artificial Intelligence (Oldenburg University).
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Nunnari, F., Alam, H.M.T., Sonntag, D. (2021). Anomaly Detection for Skin Lesion Images Using Replicator Neural Networks. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds) Machine Learning and Knowledge Extraction. CD-MAKE 2021. Lecture Notes in Computer Science(), vol 12844. Springer, Cham. https://doi.org/10.1007/978-3-030-84060-0_15
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DOI: https://doi.org/10.1007/978-3-030-84060-0_15
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