@inproceedings{ad24d06494a145db9cfd29b8d8d12f93,
title = "Ensembles of multiple models and architectures for robust brain tumour segmentation",
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.",
author = "K. Kamnitsas and W. Bai and E. Ferrante and S. McDonagh and M. Sinclair and N. Pawlowski and M. Rajchl and M. Lee and B. Kainz and D. Rueckert and B. Glocker",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 3rd International Workshop on Brainlesion, BrainLes 2017 Held in Conjunction with Medical Image Computing for Computer Assisted Intervention , MICCAI 2017 ; Conference date: 14-09-2017 Through 14-09-2017",
year = "2018",
doi = "10.1007/978-3-319-75238-9\_38",
language = "English",
isbn = "9783319752372",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "450--462",
editor = "Bjoern Menze and Alessandro Crimi and Hugo Kuijf and Mauricio Reyes and Spyridon Bakas",
booktitle = "Brainlesion",
}