Publikation
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.
Zusammenfassung
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.
Projekte
SkinCare (EIT) - SkinCare,
pAItient - Innovationszentrum mit integrierter, rechtssicherer Umgebung zur Entwicklung, Testung, und klinischen Bewertung Kl-basierter Anwendungen.
pAItient - Innovationszentrum mit integrierter, rechtssicherer Umgebung zur Entwicklung, Testung, und klinischen Bewertung Kl-basierter Anwendungen.