activity
20162019
most citedReal-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

211 citations · 246 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CV20191 cited

Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations

Chen Qin, Bibo Shi, Rui Liao +3

We propose a fully unsupervised multi-modal deformable image registration method (UMDIR), which does not require any ground truth deformation fields or any aligned multi-modal imag…

cs.CV2019

3D High-Resolution Cardiac Segmentation Reconstruction from 2D Views using Conditional Variational Autoencoders

Carlo Biffi, Juan J. Cerrolaza, Giacomo Tarroni +4

Accurate segmentation of heart structures imaged by cardiac MR is key for the quantitative analysis of pathology. High-resolution 3D MR sequences enable whole-heart structural imag…

cs.CV20191 cited

Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study

Robert Robinson, Vanya V. Valindria, Wenjia Bai +19

Background: The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic proc…

cs.CV2018

Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review

Juan J. Cerrolaza, Mirella Lopez-Picazo, Ludovic Humbert +4

The medical image analysis field has traditionally been focused on the development of organ-, and disease-specific methods. Recently, the interest in the development of more 20 com…

q-bio.TO201718 cited

White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks

R. Guerrero, C. Qin, O. Oktay +8

The accurate assessment of White matter hyperintensities (WMH) burden is of crucial importance for epidemiological studies to determine association between WMHs, cognitive and clin…

cs.CV20168 cited

Unsupervised domain adaptation in brain lesion segmentation with adversarial networks

Konstantinos Kamnitsas, Christian Baumgartner, Christian Ledig +8

Significant advances have been made towards building accurate automatic segmentation systems for a variety of biomedical applications using machine learning. However, the performan…