Ensembles of multiple models and architectures for robust brain tumour segmentation
Kamnitsas K, Bai W, Ferrante E, Mcdonagh S, Sinclair M, Pawlowski N, Rajchl M, Lee M, Kainz B, Rueckert D, Glocker B (2018)
Publication Type: Conference contribution
Publication year: 2018
Journal
Publisher: Springer Verlag
Book Volume: 10670 LNCS
Pages Range: 450-462
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Quebec City, QC, CAN
ISBN: 9783319752372
DOI: 10.1007/978-3-319-75238-9_38
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.
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United Kingdom (GB)How to cite
APA:
Kamnitsas, K., Bai, W., Ferrante, E., Mcdonagh, S., Sinclair, M., Pawlowski, N.,... Glocker, B. (2018). Ensembles of multiple models and architectures for robust brain tumour segmentation. In Bjoern Menze, Alessandro Crimi, Hugo Kuijf, Mauricio Reyes, Spyridon Bakas (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 450-462). Quebec City, QC, CAN: Springer Verlag.
MLA:
Kamnitsas, Konstantinos, et al. "Ensembles of multiple models and architectures for robust brain tumour segmentation." Proceedings of the 3rd International Workshop on Brainlesion, BrainLes 2017 Held in Conjunction with Medical Image Computing for Computer Assisted Intervention , MICCAI 2017, Quebec City, QC, CAN Ed. Bjoern Menze, Alessandro Crimi, Hugo Kuijf, Mauricio Reyes, Spyridon Bakas, Springer Verlag, 2018. 450-462.
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