Self-supervised 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
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Artificial Intelligence and Robotics; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/7037; https://ink.library.smu.edu.sg/context/sis_research/article/8040/viewcontent/2109.01303.pdf
• Institution: Singapore Management University

Unsupervised anomaly detection (UAD) that requires only normal (healthy) training images is an important tool for enabling the development of medical image analysis (MIA) applications, such as disease screening, since it is often difficult to collect and annotate abnormal (or disease) images in MIA....