Ensembles of densely-connected CNNs with label-uncertainty for brain tumor segmentation

Abstract

We introduce a new family of classifiers based on our previous DeepSCAN architecture, in which densely connected blocks of dilated convolutions are embedded in a shallow U-net-style structure of down/upsampling and skip connections. These networks are trained using a newly designed loss function which models label noise and uncertainty. We present results on the testing dataset of the Multimodal Brain Tumor Segmentation Challenge 2018.