1 citations · 1 across the 3 of their papers we have counts for
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A Field of Experts Prior for Adapting Neural Networks at Test Time
Neerav Karani, Georg Brunner, Ertunc Erdil +4
Performance of convolutional neural networks (CNNs) in image analysis tasks is often marred in the presence of acquisition-related distribution shifts between training and test ima…
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…
Image Segmentation with Pseudo-marginal MCMC Sampling and Nonparametric Shape Priors
Ertunc Erdil, Sinan Yildirim, Tolga Tasdizen +1
In this paper, we propose an efficient pseudo-marginal Markov chain Monte Carlo (MCMC) sampling approach to draw samples from posterior shape distributions for image segmentation.…