Constrained contrastive distribution learning for unsupervised anomaly detection and localisation in medical images

• Main Authors: TIAN, Yu, PANG, Guansong, LIU, Fengbei, CHEN, Yuanhong, SHIN, Seon Ho, VERJANS, Johan W., SINGH, Rajvinder
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Anomaly detection; Unsupervised learning; Lesion detection and segmentation; Self-supervised pre-training; Colonoscopy; Artificial Intelligence and Robotics; Graphics and Human Computer Interfaces; Health Information Technology
• Online Access: https://ink.library.smu.edu.sg/sis_research/7035; https://ink.library.smu.edu.sg/context/sis_research/article/8038/viewcontent/521400_1_En_Print.indd.pdf
• Institution: Singapore Management University

Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively with normal (i.e., healthy) images to detect any abnormal (i.e., unhealthy) samples that do not conform to the expected normal patterns. UAD has two main advantages over its fully supervised counterpart. Firstly, it is abl...