Deep learning models have proven their ability to perform powerful classification and segmentation considering both 2d and 3D datasets. However, these classifications are often assumed to be accurate, without providing how reliable are these decisions made by the model. There are some possible techniques to convert a stochastic deep learning model into a probabilistic one, to provide a distribution over the weights, instead of finding point estimation for them, leading to an output distribution. Such distribution over output of a model shows not only the true class but also its reliability as the variance of the distribution.
