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Deep neural networks have shown great achievements in solving complexproblems. However, there are fundamental problems that limit their real worldapplications. Lack of measurable criteria for estimating uncertainty in thenetwork outputs is one of these problems. In this paper, we address thislimitation by introducing deformation to the network input and measuring thelevel of stability in the network's output. We calculate simple randomtransformations to estimate the prediction uncertainty of deep convolutionalneural networks. For a real use-case, we apply this method to left ventriclesegmentation in MRI cardiac images. We also propose an adaptive thresholdingmethod to consider the deep neural network uncertainty. Experimental resultsdemonstrate state-of-the-art performance and highlight the capabilities ofsimple methods in conjunction with deep neural networks.

Left ventricle segmentation By modelling uncertainty in prediction of deep convolutional neural networks and adaptive thresholding inference