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Found 4 papers, 2 papers with code
Resource and data efficient self supervised learning
We investigate the utility of pretraining by contrastive self supervised learning on both natural-scene and medical imaging datasets when the unlabeled dataset size is small, or when the diversity within the unlabeled set does not lead to better representations.
Overcoming the limitations of patch-based learning to detect cancer in whole slide images
We apply a binary cancer detection network on post neoadjuvant therapy breast cancer WSIs to find the tumor bed outlining the extent of cancer, a task which requires sensitivity and precision across the whole slide.
Self supervised contrastive learning for digital histopathology
In this paper, we use a contrastive self-supervised learning method called SimCLR that achieved state-of-the-art results on natural-scene images and apply this method to digital histopathology by collecting and pretraining on 57 histopathology datasets without any labels.
Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant Network
This paper presents a meta-learning framework for few-shots One-Class Classification (OCC) at test-time, a setting where labeled examples are only available for the positive class, and no supervision is given for the negative example.
