  Deep learning approaches such as convolutional neural nets have consistently
outperformed previous methods on challenging tasks such as dense, semantic
segmentation. However, the various proposed networks perform differently, with
behaviour largely influenced by architectural choices and training settings.
This paper explores Ensembles of Multiple Models and Architectures (EMMA) for
robust performance through aggregation of predictions from a wide range of
methods. The approach reduces the influence of the meta-parameters of
individual models and the risk of overfitting the configuration to a particular
database. EMMA can be seen as an unbiased, generic deep learning model which is
shown to yield excellent performance, winning the first position in the BRATS
2017 competition among 50+ participating teams.
