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

• Main Authors: TIAN, Yu, LIU, Fengbei, PANG, Guansong, CHEN, Yuanhong, LIU, Yuyuan, VERJANS, Johan W., SINGH, Rajvinder, CARNEIRO, Gustavo
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Anomaly segmentation; Colonoscopy; Covid-19; Fundus image; Lesion segmentation; One-class classification; Self-supervised learning; Unsupervised anomaly detection; Databases and Information Systems; Graphics and Human Computer Interfaces; Medical Sciences
• Online Access: https://ink.library.smu.edu.sg/sis_research/8142; https://ink.library.smu.edu.sg/context/sis_research/article/9145/viewcontent/Self_supervised_Pseudo_Medical_sv.pdf
• Institution: Singapore Management University

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 applied in disease screening problems because the traini...