Large-scale inference of liver fat with neural networks on UK Biobank body MRI

[1]  Johan Wikström,et al.  Identifying Morphological Indicators of Aging With Neural Networks on Large-Scale Whole-Body MRI , 2020, IEEE Transactions on Medical Imaging.

[2]  J. Kullberg,et al.  Large-scale biometry with interpretable neural network regression on UK Biobank body MRI , 2020, Scientific Reports.

[3]  Filip Malmberg,et al.  Fast Graph-Cut Based Optimization for Practical Dense Deformable Registration of Volume Images , 2018, Comput. Medical Imaging Graph..

[4]  H. H. Thodberg,et al.  The RSNA Pediatric Bone Age Machine Learning Challenge. , 2019, Radiology.

[5]  N. Obuchowski,et al.  Linearity, Bias, and Precision of Hepatic Proton Density Fat Fraction Measurements by Using MR Imaging: A Meta-Analysis. , 2017, Radiology.

[6]  Michael V. McConnell,et al.  Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning , 2017, Nature Biomedical Engineering.

[7]  Michael Brady,et al.  Deep Quantitative Liver Segmentation and Vessel Exclusion to Assist in Liver Assessment , 2017, MIUA.

[8]  Wufeng Xue,et al.  Direct Multitype Cardiac Indices Estimation via Joint Representation and Regression Learning , 2017, IEEE Transactions on Medical Imaging.

[9]  S. Reeder,et al.  Multisite, multivendor validation of the accuracy and reproducibility of proton‐density fat‐fraction quantification at 1.5T and 3T using a fat–water phantom , 2017, Magnetic resonance in medicine.

[10]  Stefan Neubauer,et al.  Characterisation of liver fat in the UK Biobank cohort , 2017, PloS one.

[11]  Giovanni Montana,et al.  Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker , 2016, NeuroImage.

[12]  Magnus Borga,et al.  Feasibility of MR-Based Body Composition Analysis in Large Scale Population Studies , 2016, PloS one.

[13]  L. Henry,et al.  Global epidemiology of nonalcoholic fatty liver disease—Meta‐analytic assessment of prevalence, incidence, and outcomes , 2016, Hepatology.

[14]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  H. Schild,et al.  Comparison between modified Dixon MRI techniques, MR spectroscopic relaxometry, and different histologic quantification methods in the assessment of hepatic steatosis , 2015, European Radiology.

[16]  P. Elliott,et al.  UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age , 2015, PLoS medicine.

[17]  A. McCullough,et al.  The relevance of liver histology to predicting clinically meaningful outcomes in nonalcoholic steatohepatitis. , 2012, Clinics in liver disease.

[18]  S. Reeder,et al.  Quantitative Assessment of Liver Fat with Magnetic Resonance Imaging and Spectroscopy. , 2011, Journal of magnetic resonance imaging : JMRI.

[19]  Jonathan C. Cohen,et al.  Prevalence of hepatic steatosis in an urban population in the United States: Impact of ethnicity , 2004, Hepatology.