  Despite the astonishing performance of deep-learning based approaches for
visual tasks such as semantic segmentation, they are known to produce
miscalibrated predictions, which could be harmful for critical decision-making
processes. Ensemble learning has shown to not only boost the performance of
individual models but also reduce their miscalibration by averaging independent
predictions. In this scenario, model diversity has become a key factor, which
facilitates individual models converging to different functional solutions. In
this work, we introduce Orthogonal Ensemble Networks (OEN), a novel framework
to explicitly enforce model diversity by means of orthogonal constraints. The
proposed method is based on the hypothesis that inducing orthogonality among
the constituents of the ensemble will increase the overall model diversity. We
resort to a new pairwise orthogonality constraint which can be used to
regularize a sequential ensemble training process, resulting on improved
predictive performance and better calibrated model outputs. We benchmark the
proposed framework in two challenging brain lesion segmentation tasks --brain
tumor and white matter hyper-intensity segmentation in MR images. The
experimental results show that our approach produces more robust and
well-calibrated ensemble models and can deal with challenging tasks in the
context of biomedical image segmentation.
