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Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2017)

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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.

W. Bai, E. Ferrante, S. McDonagh and M. Sinclair—Equal contribution, in alphabetical order.

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Notes

  1. 1.

    Implementation and configuration details considered less important for this work were omitted to avoid cluttering the manuscript.

  2. 2.

    Leaderboard: https://www.cbica.upenn.edu/BraTS17/lboardValidation.html.

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Acknowledgements

This work is supported by the EPSRC (EP/N023668/1, EP/N024494/1 and EP/P001009/1) and partially funded under the 7th Framework Programme by the European Commission (CENTER-TBI: https://www.center-tbi.eu/). KK is supported by the President’s PhD Scholarship of Imperial College London. EF is beneficiary of an AXA Research Fund postdoctoral grant. NP is supported by Microsoft Research through its PhD Scholarship Programme and the EPSRC Centre for Doctoral Training in High Performance Embedded and Distributed Systems (HiPEDS, Grant Reference EP/L016796/1). We gratefully acknowledge the support of NVIDIA with the donation of GPUs for our research.

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Authors and Affiliations

  1. Biomedical Image Analysis Group, Imperial College London, London, UK

    K. Kamnitsas, W. Bai, E. Ferrante, S. McDonagh, M. Sinclair, N. Pawlowski, M. Rajchl, M. Lee, B. Kainz, D. Rueckert & B. Glocker

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  1. K. Kamnitsas
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  2. W. Bai
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  3. E. Ferrante
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  4. S. McDonagh
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  5. M. Sinclair
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  6. N. Pawlowski
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  7. M. Rajchl
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  8. M. Lee
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  9. B. Kainz
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  10. D. Rueckert
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  11. B. Glocker
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Corresponding author

Correspondence to K. Kamnitsas .

Editor information

Editors and Affiliations

  1. African Institute of Mathematical Sciences, Cape Town, Ghana

    Alessandro Crimi

  2. University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Spyridon Bakas

  3. University Medical Center Utrecht, Utrecht, The Netherlands

    Hugo Kuijf

  4. Technical University Munich, Munich, Germany

    Bjoern Menze

  5. Universität Bern, Bern, Switzerland

    Mauricio Reyes

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Kamnitsas, K. et al. (2018). Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017. Lecture Notes in Computer Science(), vol 10670. Springer, Cham. https://doi.org/10.1007/978-3-319-75238-9_38

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  • DOI: https://doi.org/10.1007/978-3-319-75238-9_38

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