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Global Contrast Masked Autoencoders Are Powerful Pathological Representation Learners. (arXiv:2205.09048v1 [cs.CV])
May 19, 2022, 1:10 a.m. | Hao Quan, Xingyu Li, Weixing Chen, Mingchen Zou, Ruijie Yang, Tingting Zheng, Ruiqun Qi, Xinghua Gao, Xiaoyu Cui
cs.CV updates on arXiv.org arxiv.org
Based on digital whole slide scanning technique, artificial intelligence
algorithms represented by deep learning have achieved remarkable results in the
field of computational pathology. Compared with other medical images such as
Computed Tomography (CT) or Magnetic Resonance Imaging (MRI), pathological
images are more difficult to annotate, thus there is an extreme lack of data
sets that can be used for supervised learning. In this study, a self-supervised
learning (SSL) model, Global Contrast Masked Autoencoders (GCMAE), is proposed,
which has the …
More from arxiv.org / cs.CV updates on arXiv.org
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