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Self-supervised driven consistency training for annotation efficient histopathology image analysis
[article]
2021
arXiv
pre-print
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 learn-ing unsupervised feature representations, they still struggle to generalize well to downstream tasks when the number of labeled instances is small. In this
arXiv:2102.03897v3
fatcat:gydyinplx5gttgziblpdmi6vnm