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Abstract #2399

Unsupervised reconstruction based anomaly detection using a Variational Auto Encoder

Soumick Chatterjee1,2,3, Alessandro Sciarra1,4, Max Dünnwald3,4, Shubham Kumar Agrawal3, Pavan Tummala3, Disha Setlur3, Aman Kalra3, Aishwarya Jauhari3, Steffen Oeltze-Jafra4,5,6, Oliver Speck1,5,6,7, and Andreas Nürnberger2,3,6
1Department of Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg, Germany, 2Data and Knowledge Engineering Group, Otto von Guericke University, Magdeburg, Germany, 3Faculty of Computer Science, Otto von Guericke University, Magdeburg, Germany, 4MedDigit, Department of Neurology, Medical Faculty, University Hopspital, Magdeburg, Germany, 5German Centre for Neurodegenerative Diseases, Magdeburg, Germany, 6Center for Behavioral Brain Sciences, Magdeburg, Germany, 7Leibniz Institute for Neurobiology, Magdeburg, Germany

While commonly used approach for disease localization, we propose an approach to detect anomalies by differentiating them from reliable models of anatomies without pathologies. The method is based on a Variational Auto Encoder to learn the anomaly free distribution of the anatomy and a novel image subtraction approach to obtain pixel-precise segmentation of the anomalous regions. The proposed model has been trained with the MOOD dataset. Evaluation is done on BraTS 2019 dataset and a subset of the MOOD, which contain anomalies to be detected by the model.

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