Autoencoders for unsupervised anomaly segmentation in brain MR images: A comparative study

人工智能 计算机科学 模式识别(心理学) 分割 异常检测 无监督学习 可比性 领域(数学) 代表(政治) 机器学习 领域(数学分析) 排名(信息检索) 深度学习 数学 数学分析 组合数学 政治 政治学 纯数学 法学
作者
Christoph Baur,Stefan Denner,Benedikt Wiestler,Nassir Navab,Shadi Albarqouni
出处
期刊:Medical Image Analysis [Elsevier BV]
卷期号:69: 101952-101952 被引量:260
标识
DOI:10.1016/j.media.2020.101952
摘要

Abstract Deep unsupervised representation learning has recently led to new approaches in the field of Unsupervised Anomaly Detection (UAD) in brain MRI. The main principle behind these works is to learn a model of normal anatomy by learning to compress and recover healthy data. This allows to spot abnormal structures from erroneous recoveries of compressed, potentially anomalous samples. The concept is of great interest to the medical image analysis community as it i) relieves from the need of vast amounts of manually segmented training data—a necessity for and pitfall of current supervised Deep Learning—and ii) theoretically allows to detect arbitrary, even rare pathologies which supervised approaches might fail to find. To date, the experimental design of most works hinders a valid comparison, because i) they are evaluated against different datasets and different pathologies, ii) use different image resolutions and iii) different model architectures with varying complexity. The intent of this work is to establish comparability among recent methods by utilizing a single architecture, a single resolution and the same dataset(s). Besides providing a ranking of the methods, we also try to answer questions like i) how many healthy training subjects are needed to model normality and ii) if the reviewed approaches are also sensitive to domain shift. Further, we identify open challenges and provide suggestions for future community efforts and research directions.
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