Abstract #0172
StRegA: Unsupervised Anomaly Detection in Brain MRIs using Compact Context-encoding Variational Autoencoder
Soumick Chatterjee1,2,3, Alessandro Sciarra1,4, Max Dünnwald3,4, Pavan Tummala3, Shubham Kumar Agrawal3, Aishwarya Jauhari3, Aman Kalra3, Steffen Oeltze-Jafra4,5, Oliver Speck1,5,6,7, and Andreas Nürnberger2,3,5
1Department of Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, Magdeburg, Germany, 2Data and Knowledge Engineering Group, Otto von Guericke University Magdeburg, Magdeburg, Germany, 3Faculty of Computer Science, Otto von Guericke University Magdeburg, Magdeburg, Germany, 4MedDigit, Department of Neurology, Medical Faculty, University Hospital, Magdeburg, Germany, 5Center for Behavioral Brain Sciences, Magdeburg, Germany, 6German Centre for NeurodegenerativeDiseases, Magdeburg, Germany, 7Leibniz Institute for Neurobiology, Magdeburg, Germany
Synopsis
Deep learning methods are typically trained in a supervised with annotated data for analysing medical images with the motivation of detecting pathologies. In the absence of manually annotated training data, unsupervised anomaly detection can be one of the possible solutions. This work proposes StRegA, an unsupervised anomaly detection pipeline based on a compact ceVAE and shows its applicability in detecting anomalies such as tumours in brain MRIs. The proposed pipeline achieved a Dice score of 0.642±0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859±0.112 while detecting artificially induced anomalies.
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