8 citations · 9 across the 2 of their papers we have counts for
3 papers · 1 filter
Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation
Krishna Chaitanya, Ertunc Erdil, Neerav Karani +1
Supervised deep learning-based methods yield accurate results for medical image segmentation. However, they require large labeled datasets for this, and obtaining them is a laborio…
Contrastive Learning of Single-Cell Phenotypic Representations for Treatment Classification
Alexis Perakis, Ali Gorji, Samriddhi Jain +3
Learning robust representations to discriminate cell phenotypes based on microscopy images is important for drug discovery. Drug development efforts typically analyse thousands of…
Imbalance-Aware Self-Supervised Learning for 3D Radiomic Representations
Hongwei Li, Fei-Fei Xue, Krishna Chaitanya +5
Radiomic representations can quantify properties of regions of interest in medical image data. Classically, they account for pre-defined statistics of shape, texture, and other low…