Constrained contrastive distribution learning for unsupervised anomaly detection and localisation in medical images
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...
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Main Authors: | , , , , , , |
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Format: | text |
Language: | English |
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Institutional Knowledge at Singapore Management University
2021
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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 |
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Institution: | Singapore Management University |
Language: | English |