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Unsupervised reconstruction based anomaly detection using a Variational Auto
Encoder
Department of Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg, Germany,
Data and Knowledge Engineering Group, Otto von Guericke University, Magdeburg, Germany,
Faculty of
Computer Science, Otto von Guericke University, Magdeburg, Germany,
MedDigit, Department of Neurology, Medical Faculty, University Hopspital, Magdeburg, Germany,
German Centre for Neurodegenerative
Diseases, Magdeburg, Germany,
Center for Behavioral Brain Sciences, Magdeburg, Germany,
Leibniz Institute for Neurobiology, Magdeburg, Germany
Synopsis
While commonly used approach for disease localization, we propose an approach to detect anomalies by di�erentiating 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.
Introduction
Methods
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Results and Discussion
Conclusion and future work
Acknowledgements
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References
Figures
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