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Out-of-distribution Detection in Medical Image Analysis: A survey
April 30, 2024, 4:47 a.m. | Zesheng Hong, Yubiao Yue, Yubin Chen, Huanjie Lin, Yuanmei Luo, Mini Han Wang, Weidong Wang, Jialong Xu, Xiaoqi Yang, Zhenzhang Li, Sihong Xie
cs.CV updates on arXiv.org arxiv.org
Abstract: Computer-aided diagnostics has benefited from the development of deep learning-based computer vision techniques in these years. Traditional supervised deep learning methods assume that the test sample is drawn from the identical distribution as the training data. However, it is possible to encounter out-of-distribution samples in real-world clinical scenarios, which may cause silent failure in deep learning-based medical image analysis tasks. Recently, research has explored various out-of-distribution (OOD) detection situations and techniques to enable a trustworthy …
abstract analysis arxiv clinical computer computer vision cs.cv data deep learning detection development diagnostics distribution however image medical sample samples survey test training training data type vision world
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