BayesNetCNN: incorporating uncertainty in neural networks for image-based classification tasks

The willingness to trust predictions formulated by automatic algorithms is key in a vast number of domains. We propose converting a standard neural network into a Bayesian neural network and estimating the variability of predictions by sampling different networks at inference time. We use a rejection-based approach to increase classification accuracy from 0.86 to 0.95 while retaining 75% of the test set for Alzheimer disease classification from MRI morphometry images. Estimating uncertainty of a prediction and modulating the behavior of the network to a desirable degree of confidence, represents a crucial step in the direction of responsible and trustworthy AI.