Self-supervised driven consistency training for annotation efficient histopathology image analysis

Volume: 75, Pages: 102256 - 102256
Published: Jan 1, 2022
Abstract
Training a neural network with a large labeled dataset is still a dominant paradigm in computational histopathology. However, obtaining such exhaustive manual annotations is often expensive, laborious, and prone to inter and intra-observer variability. While recent self-supervised and semi-supervised methods can alleviate this need by learning unsupervised feature representations, they still struggle to generalize well to downstream tasks when...
Paper Details
Title
Self-supervised driven consistency training for annotation efficient histopathology image analysis
Published Date
Jan 1, 2022
Volume
75
Pages
102256 - 102256
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