  Fully convolutional U-shaped neural networks have largely been the dominant
approach for pixel-wise image segmentation. In this work, we tackle two defects
that hinder their deployment in real-world applications: 1) Predictions lack
uncertainty quantification that may be crucial to many decision-making systems;
2) Large memory storage and computational consumption demanding extensive
hardware resources. To address these issues and improve their practicality we
demonstrate a few-parameter compact Bayesian convolutional architecture, that
achieves a marginal improvement in accuracy in comparison to related work using
significantly fewer parameters and compute operations. The architecture
combines parameter-efficient operations such as separable convolutions,
bilinear interpolation, multi-scale feature propagation and Bayesian inference
for per-pixel uncertainty quantification through Monte Carlo Dropout. The best
performing configurations required fewer than 2.5 million parameters on diverse
challenging datasets with few observations.
