abstract
- Deep neural networks have shown great achievements in solving complex problems. However, there are fundamental challenges which limit their real world applications. Lack of a measurable criterion for estimating uncertainty of the network predictions is one of these challenges. However, we can compute the variance of the network output by applying spatial transformations, distortions or noise injection to network inputs and interpret these variances as uncertainty of the network predictions. In other words, as long as the deformations do not conceptually alter target of interest, we expect the network to produce the same result. Hence, any outputs changes can be a sign of uncertainty in the network predictions. In order to estimate the prediction uncertainty of deep convolutional neural networks we use simple random transformations. By exploiting the network uncertainty, we improve the overall performance of the system. For a real use case, we apply the proposed method to segment left ventricle in MRI cardiac images. Experimental results demonstrate state-of- the-art performance and highlight the potential capabilities of simple ideas in conjunction with deep neural networks.