  Convolutional Neural Networks (CNNs) are propelling advances in a range of
different computer vision tasks such as object detection and object
segmentation. Their success has motivated research in applications of such
models for medical image analysis. If CNN-based models are to be helpful in a
medical context, they need to be precise, interpretable, and uncertainty in
predictions must be well understood. In this paper, we develop and evaluate
recent advances in uncertainty estimation and model interpretability in the
context of semantic segmentation of polyps from colonoscopy images. We evaluate
and enhance several architectures of Fully Convolutional Networks (FCNs) for
semantic segmentation of colorectal polyps and provide a comparison between
these models. Our highest performing model achieves a 76.06\% mean IOU accuracy
on the EndoScene dataset, a considerable improvement over the previous
state-of-the-art.
