The ability of generating model uncertainty for a predictive system on each prediction is crucial for decision-making, especially in the field of medicine, but it has been a missing part in conventional deep learning models. We propose the utilization of Bayesian deep learning, which combines Monte Carlo dropout layers with the original deep neural network at testing time to enable model uncertainty generation. Its prediction accuracy and the behavior of uncertainty were studied on MRI brain extraction. Its segmentation accuracy outperforms 6 popular methods, and the uncertainty’s reactions to different training set sizes and inconsistent training labels meet the expectation well.
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