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Large-scale medical studies such as the UK Biobank examine thousands ofvolunteer participants with medical imaging techniques. Combined with the vastamount of collected metadata, anatomical information from these images has thepotential for medical analyses at unprecedented scale. However, theirevaluation often requires manual input and long processing times, limiting theamount of reference values for biomarkers and other measurements available forresearch. Recent approaches with convolutional neural networks for regressioncan perform these evaluations automatically. On magnetic resonance imaging(MRI) data of more than 40,000 UK Biobank subjects, these systems can estimatehuman age, body composition and more. This style of analysis is almost entirelydata-driven and no manual intervention or guidance with manually segmentedground truth images is required. The networks often closely emulate thereference method that provided their training data and can reach levels ofagreement comparable to the expected variability between established medicalgold standard techniques. The risk of silent failure can be individuallyquantified by predictive uncertainty obtained from a mean-variance criterionand ensembling. Saliency analysis furthermore enables an interpretation of theunderlying relevant image features and showed that the networks learned tocorrectly target specific organs, limbs, and regions of interest.

Deep regression for uncertainty-aware and interpretable analysis of large-scale body MRI