Search Results for author:
Found 6 papers, 1 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.
Deep neural network models for computational histopathology: A survey
Histopathological images contain rich phenotypic information that can be used to monitor underlying mechanisms contributing to diseases progression and patient survival outcomes.
Learning to segment images with classification labels
Annotating data for segmentation is generally considered to be more laborious as the annotator has to draw around the boundaries of regions of interest, as opposed to assigning image patches a class label.
Multi-layer Domain Adaptation for Deep Convolutional Networks
Despite their success in many computer vision tasks, convolutional networks tend to require large amounts of labeled data to achieve generalization.
