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Found 2 papers, 1 papers with code
Hierarchical Text-to-Vision Self Supervised Alignment for Improved Histopathology Representation Learning
Self-supervised representation learning has been highly promising for histopathology image analysis with numerous approaches leveraging their patient-slide-patch hierarchy to learn better representations.
Contrastive Deep Encoding Enables Uncertainty-aware Machine-learning-assisted Histopathology
We show that our approach can reach the state-of-the-art (SOTA) for patch-level classification with only 1-10% randomly selected annotations compared to other SOTA approaches.
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