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First Name:
xiangyu
Last Name:
xiong
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Modern deep neural networks are overparameterized and thus require data augmentation techniques to prevent over-fitting and improve generalization ability. Generative adversarial networks (GANs) are famous for generating visually realistic images. However, the generated images lack diversity and have uncertain class labels. On the other hand, recent methods mix labels proportionally to the salient region.
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Contrast-enhanced computed tomography (CE-CT) is the gold standard for diagnosing AD. However, contrast agents can cause allergic reactions or renal failure in some patients. Moreover, AD diagnosis by radiologists using non- contrast-enhanced CT (NCE-CT) images has poor sensitivity. To address this issue, a novel deep learning methos was proposed for AD detection using NCE-CT volumes. It may have great potential to reduce the misdiagnosis of AD using NCE-CT in clinical practice.
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