StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder. (arXiv:2201.13271v2 [eess.IV] UPDATED)

Expert interpretation of anatomical images of the human brain is the central
part of neuro-radiology. Several machine learning-based techniques have been
proposed to assist in the analysis process. However, the ML models typically
need to be trained to perform a specific task, e.g., brain tumour segmentation
or classification. Not only do the corresponding training data require
laborious manual annotations, but a wide variety of abnormalities can be
present in a human brain MRI - even more than one simultaneously, which …

anomaly anomaly detection arxiv autoencoder brain context detection encoding unsupervised