190 citations · 208 across the 5 of their papers we have counts for
5 papers
Assessing radiomics feature stability with simulated CT acquisitions
Kyriakos Flouris, Oscar Jimenez-del-Toro, Christoph Aberle +7
Medical imaging quantitative features had once disputable usefulness in clinical studies. Nowadays, advancements in analysis techniques, for instance through machine learning, have…
Sampling possible reconstructions of undersampled acquisitions in MR imaging
Kerem C. Tezcan, Neerav Karani, Christian F. Baumgartner +1
Undersampling the k-space during MR acquisitions saves time, however results in an ill-posed inversion problem, leading to an infinite set of images as possible solutions. Traditio…
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…
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…