765 citations · 1.5k across the 13 of their papers we have counts for
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Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction
Chen Qin, Jo Schlemper, Jose Caballero +3
Accelerating the data acquisition of dynamic magnetic resonance imaging (MRI) leads to a challenging ill-posed inverse problem, which has received great interest from both the sign…
Frame Interpolation with Multi-Scale Deep Loss Functions and Generative Adversarial Networks
Joost van Amersfoort, Wenzhe Shi, Alejandro Acosta +4
Frame interpolation attempts to synthesise frames given one or more consecutive video frames. In recent years, deep learning approaches, and notably convolutional neural networks,…
Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize
Andrew Aitken, Christian Ledig, Lucas Theis +3
The most prominent problem associated with the deconvolution layer is the presence of checkerboard artifacts in output images and dense labels. To combat this problem, smoothness c…
Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation
Ozan Oktay, Enzo Ferrante, Konstantinos Kamnitsas +10
Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where image…
A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction
Jo Schlemper, Jose Caballero, Joseph V. Hajnal +2
Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2D cardiac magnetic resonance (MR) images from undersampled data using…
A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction
Jo Schlemper, Jose Caballero, Joseph V. Hajnal +2
The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow. Inspired by recent advances in deep learning, we propose a framework for reconstructing MR images from under…