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Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with impured training data
[article]
2022
arXiv
pre-print
The detection of lesions in magnetic resonance imaging (MRI)-scans of human brains remains challenging, time-consuming and error-prone. Recently, unsupervised anomaly detection (UAD) methods have shown promising results for this task. These methods rely on training data sets that solely contain healthy samples. Compared to supervised approaches, this significantly reduces the need for an extensive amount of labeled training data. However, data labelling remains error-prone. We study how
arXiv:2204.05778v1
fatcat:imx3hipiovddfhymf2whqidita