The uncertainty in the segmentation decisions is captured by the covariance matrix of the predictive distribution.
We show that Bayesian neural networks automatically discover redundancy in model parameters, thus enabling self-compression, which is linked to the propagation of uncertainty through the layers of the network.
Learning in uncertain, noisy, or adversarial environments is a challenging task for deep neural networks (DNNs).
Magnetic resonance imaging (MRI) is routinely used for brain tumor diagnosis, treatment planning, and post-treatment surveillance.
The architectures of deep artificial neural networks (DANNs) are routinely studied to improve their predictive performance.