241 citations · 264 across the 11 of their papers we have counts for
4 papers · 1 filter
RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation
Marc Gantenbein, Ertunc Erdil, Ender Konukoglu
Quantifying segmentation uncertainty has become an important issue in medical image analysis due to the inherent ambiguity of anatomical structures and its pathologies. Recently, n…
Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE
Anna Volokitin, Ertunc Erdil, Neerav Karani +4
Probabilistic modelling has been an essential tool in medical image analysis, especially for analyzing brain Magnetic Resonance Images (MRI). Recent deep learning techniques for es…
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…