Global Contrast Masked Autoencoders Are Powerful Pathological Representation Learners. (arXiv:2205.09048v1 [cs.CV])

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 …

arxiv cv global representation