Web: http://arxiv.org/abs/2205.04723

May 11, 2022, 1:10 a.m. | Cheng Xue, Lequan Yu, Pengfei Chen, Qi Dou, Pheng-Ann Heng

cs.CV updates on arXiv.org arxiv.org

Deep neural networks have achieved remarkable success in a wide variety of
natural image and medical image computing tasks. However, these achievements
indispensably rely on accurately annotated training data. If encountering some
noisy-labeled images, the network training procedure would suffer from
difficulties, leading to a sub-optimal classifier. This problem is even more
severe in the medical image analysis field, as the annotation quality of
medical images heavily relies on the expertise and experience of annotators. In
this paper, we propose …

arxiv classification data global image medical representation training

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