Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images

Yu Tian, Fengbei Liu, Guansong Pang, Yuanhong Chen, Yuyuan Liu, Johan W. Verjans, Rajvinder Singh, Gustavo Carneiro

Abstract: Highlights•A new self-supervised pre-training method for unsupervised anomaly detection algorithms.•PMSACL re-formulates the one-class classification into a pseudo multi-class problem.•A new contrastive optimisation to learn better fine-grained features for downstream anomaly detectors.•This method can adapt well to different types of downstream anomaly detection methods.•PMSACL can achieve state-of-the-art results on datasets from many medical domains.