# Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation Source: [Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:48:18.501Z Authors: Konstantinos Kamnitsas, Wenjia Bai, Enzo Ferrante, Steven McDonagh, Matthew Sinclair, Nick Pawlowski, Martin Rajchl, Matthew Lee, Bernhard Kainz, Daniel Rueckert, Ben Glocker Published: 2017-11-04T17:43:07.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Brain Tumor Segmentation on BraTS 2017 (val)](): Brain Tumor Segmentation; BraTS 2017 (val); Dice (Enh.): 73.8. ## Original sources and implementation [Original paper]() [PDF]()