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