The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation
Open accessAbstract
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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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
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Multilayer feedforward networks are universal approximators
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“Regularization reduces the variance but increases the bias, as expressed in the bias/variance dilemma [15].”
BackgroundEfficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.10.004 · Cited by 3,498 · cited 5×
“As analysed in [13], these methods strongly influence the sensitivity of the model to each class.”
BackgroundMethodInfluentialSelf-citationBakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017) · Scientific Data · doi:10.1038/sdata.2017.117 · Cited by 3,049
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2538465 · Cited by 2,735
A survey of MRI-based medical image analysis for brain tumor studies
Bauer, S., Wiest, R., Nolte, L.-P., & Reyes, M. (2013) · Physics in Medicine and Biology · doi:10.1088/0031-9155/58/13/r97 · Cited by 1,036
Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MR
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Self-citationBakas, S., Zeng, K., Sotiras, A., Rathore, S., Akbari, H., Gaonkar, B., Rozycki, M., Pati, S., & Davatzikos, C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-30858-6_13 · Cited by 101
Cited by
Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images
Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H. R., & Xu, D. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-08999-2_22 · Cited by 2,273
Attention gated networks: learning to leverage salient regions in medical images
Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., & Rueckert, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.012 · Cited by 2,014
Self-citationBakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R. T., Berger, C., Ha, S. M., Rozycki, M., et al. (2018) · Apollo - University of Cambridge Repository · doi:10.17863/cam.38755 · Cited by 1,441
Self-citation3D MRI Brain Tumor Segmentation Using Autoencoder Regularization
Myronenko, A. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-11726-9_28 · Cited by 1,283
Orlando, J. I., Fu, H., Barbosa Breda, J., van Keer, K., Bathula, D. R., Diaz-Pinto, A., Fang, R., Heng, P.-A., Kim, J., Lee, J., et al. (2020) · Medical Image Analysis · doi:10.1016/j.media.2019.101570 · Cited by 855
Metadata
- Authors
- Kamnitsas, K., Bai, W., Ferrante, E., McDonagh, S., Sinclair, M., Pawlowski, N., Rajchl, M., Lee, M., Kainz, B., Rueckert, D., & Glocker, B.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 450-462
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2767623272
- Cited by
- 434
- References
- 11
- FWCI
- 40.64
- Type
- conference paper
- Language
- en
- Open access
- green
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