22 citations · 26 across the 6 of their papers we have counts for
11 papers
HyperCMR: Enhanced Multi-Contrast CMR Reconstruction with Eagle Loss
Ruru Xu, Caner Özer, Ilkay Oksuz
Accelerating image acquisition for cardiac magnetic resonance imaging (CMRI) is a critical task. CMRxRecon2024 challenge aims to set the state of the art for multi-contrast CMR rec…
A survey on shape-constraint deep learning for medical image segmentation
Simon Bohlender, Ilkay Oksuz, Anirban Mukhopadhyay
Since the advent of U-Net, fully convolutional deep neural networks and its many variants have completely changed the modern landscape of deep learning based medical image segmenta…
Channel Attention Networks for Robust MR Fingerprinting Matching
Refik Soyak, Ebru Navruz, Eda Ozgu Ersoy +5
Magnetic Resonance Fingerprinting (MRF) enables simultaneous mapping of multiple tissue parameters such as T1 and T2 relaxation times. The working principle of MRF relies on varyin…
dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance
Jo Schlemper, Ilkay Oksuz, James R. Clough +5
AUTOMAP is a promising generalized reconstruction approach, however, it is not scalable and hence the practicality is limited. We present dAUTOMAP, a novel way for decomposing the…
Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction from K-space
lkay Oksuz, James Clough, Bram Ruijsink +7
In fully sampled cardiac MR (CMR) acquisitions, motion can lead to corruption of k-space lines, which can result in artefacts in the reconstructed images. In this paper, we propose…
Global and Local Interpretability for Cardiac MRI Classification
James R. Clough, Ilkay Oksuz, Esther Puyol-Anton +3
Deep learning methods for classifying medical images have demonstrated impressive accuracy in a wide range of tasks but often these models are hard to interpret, limiting their app…