Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images
Description
Unsupervised anomaly detection (UAD) methods are trained with normal (or healthy) images only, but during testing, they are able to classify normal and abnormal (or disease) images. UAD is an important medical image analysis (MIA) method to be
