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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