138 citations · 366 across the 14 of their papers we have counts for
9 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…
Joint reconstruction and bias field correction for undersampled MR imaging
Mélanie Gaillochet, Kerem C. Tezcan, Ender Konukoglu
Undersampling the k-space in MRI allows saving precious acquisition time, yet results in an ill-posed inversion problem. Recently, many deep learning techniques have been developed…
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…
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…
Unsupervised Lesion Detection via Image Restoration with a Normative Prior
Xiaoran Chen, Suhang You, Kerem Can Tezcan +1
Unsupervised lesion detection is a challenging problem that requires accurately estimating normative distributions of healthy anatomy and detecting lesions as outliers without trai…
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…