An uncertainty-aware active learning method built on SimCLRv2 achieves competitive cancer subtyping accuracy with only 1-10% of expert labels on two histopathology benchmarks.
Active learning for patch-based digital pathology using convolutional neural networks to reduce annotation costs
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Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology
An uncertainty-aware active learning method built on SimCLRv2 achieves competitive cancer subtyping accuracy with only 1-10% of expert labels on two histopathology benchmarks.