171 citations · 281 across the 6 of their papers we have counts for
9 papers
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…
Active label cleaning for improved dataset quality under resource constraints
Melanie Bernhardt, Daniel C. Castro, Ryutaro Tanno +9
Imperfections in data annotation, known as label noise, are detrimental to the training of machine learning models and have an often-overlooked confounding effect on the assessment…
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…
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…
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…
PHiSeg: Capturing Uncertainty in Medical Image Segmentation
Christian F. Baumgartner, Kerem C. Tezcan, Krishna Chaitanya +6
Segmentation of anatomical structures and pathologies is inherently ambiguous. For instance, structure borders may not be clearly visible or different experts may have different st…
MR image reconstruction using deep density priors
Kerem C. Tezcan, Christian F. Baumgartner, Roger Luechinger +2
Algorithms for Magnetic Resonance (MR) image reconstruction from undersampled measurements exploit prior information to compensate for missing k-space data. Deep learning (DL) prov…
Visual Feature Attribution using Wasserstein GANs
Christian F. Baumgartner, Lisa M. Koch, Kerem Can Tezcan +2
Attributing the pixels of an input image to a certain category is an important and well-studied problem in computer vision, with applications ranging from weakly supervised localis…