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15 de jul. de 2020 · This article explores the making and management of anomaly in scientific work, taking ‘medically unexplained symptoms’ (MUS) as its case. MUS is a category used to characterize health conditions that are widely held to be ambiguous, in terms of their nature, causes and treatment. It has been suggested that MUS is a ‘wastebasket diagnosis’.
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Keywords: Anomaly detection · Di usion models · Weak supervision. 1 Introduction In medical image analysis, pixel-wise annotated ground truth is hard to obtain, often unavailable and contains a bias to the human annotators. Weakly super-vised anomaly detection has gained a lot of interest in research as an essential
18 de jul. de 2021 · Abstract. Machine learning–based medical anomaly detection is an important problem that has been extensively studied. Numerous approaches have been proposed across various medical application domains and we observe several similarities across these distinct applications. Despite this comparability, we observe a lack of structured organisation ...
Diffusion Models for Medical Anomaly Detection. In medical applications, weakly supervised anomaly detection methods are of great interest, as only image-level annotations are required for training. Current anomaly detection methods mainly rely on generative adversarial networks or autoencoder models. Those models are often complicated to train ...
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