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Found 9 papers, 6 papers with code
Leveraging point annotations in segmentation learning with boundary loss
Hence it is intuitively inappropriate for weak supervision, where the ground truth label may be much smaller than the actual object and a certain amount of false positives (w. r. t.
MIMIR: Deep Regression for Automated Analysis of UK Biobank Body MRI
UK Biobank (UKB) conducts large-scale examinations of more than half a million volunteers, collecting health-related information on genetics, lifestyle, blood biochemistry, and more.
Deep regression for uncertainty-aware and interpretable analysis of large-scale body MRI
Large-scale medical studies such as the UK Biobank examine thousands of volunteer participants with medical imaging techniques.
Uncertainty-Aware Body Composition Analysis with Deep Regression Ensembles on UK Biobank MRI
The results indicate that deep regression ensembles could ultimately provide automated, uncertainty-aware measurements of body composition for more than 120, 000 UK Biobank neck-to-knee body MRI that are to be acquired within the coming years.
Large-scale inference of liver fat with neural networks on UK Biobank body MRI
These values play an important role in metabolic disease, but are only available for a minority of imaged subjects as their collection requires the careful work of image analysts on dedicated liver MRI.
Kidney segmentation in neck-to-knee body MRI of 40,000 UK Biobank participants
In this work, a pipeline for automated segmentation of parenchymal kidney volume in UK Biobank neck-to-knee body MRI is proposed.
Large-scale biometry with interpretable neural network regression on UK Biobank body MRI
In a large-scale medical examination, the UK Biobank study has successfully imaged more than 32, 000 volunteer participants with magnetic resonance imaging (MRI).
Fast Graph-Cut Based Optimization for Practical Dense Deformable Registration of Volume Images
Objective: Deformable image registration is a fundamental problem in medical image analysis, with applications such as longitudinal studies, population modeling, and atlas based image segmentation.
When can $l_p$-norm objective functions be minimized via graph cuts?
These techniques can be used to minimize objective functions written as the sum of a set of unary and pairwise terms, provided that the objective function is submodular.
