241 citations · 498 across the 10 of their papers we have counts for
4 papers · 1 filter
Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation
Krishna Chaitanya, Neerav Karani, Christian F. Baumgartner +4
Supervised learning-based segmentation methods typically require a large number of annotated training data to generalize well at test time. In medical applications, curating such d…
Task-agnostic Out-of-Distribution Detection Using Kernel Density Estimation
Ertunc Erdil, Krishna Chaitanya, Neerav Karani +1
In the recent years, researchers proposed a number of successful methods to perform out-of-distribution (OOD) detection in deep neural networks (DNNs). So far the scope of the high…
Contrastive learning of global and local features for medical image segmentation with limited annotations
Krishna Chaitanya, Ertunc Erdil, Neerav Karani +1
A key requirement for the success of supervised deep learning is a large labeled dataset - a condition that is difficult to meet in medical image analysis. Self-supervised learning…
Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation
Neerav Karani, Ertunc Erdil, Krishna Chaitanya +1
Convolutional Neural Networks (CNNs) work very well for supervised learning problems when the training dataset is representative of the variations expected to be encountered at tes…