Abstract
This work focuses on training a deep learning network in a federated learning framework. The Federated Tumor Segmentation Challenge has 2 separate tasks. Task-1 was to design an aggregation logic for a given network, which is trained in a federated learning framework. Task-2 of the challenge was to train a network that is robust and generalizable in a federated testing environment. 341 subjects were used for training both tasks of the challenge. This data was distributed across 17 collaborators, which were then used to train an individual network for each collaborator. A new weight aggregation logic was developed. The network weights in this logic were determined based on the average validation dice scores of each collaborator. A concise model was obtained using the developed weighted aggregation logic. The Dice scores for task-1 on the validation dataset for whole tumor, tumor core, and enhancing tumor were 0.767, 0.612, and 0.628 respectively. The Dice scores for task-2 on the validation dataset for whole tumor, tumor core, and enhancing tumor were 0.874, 0.773, and 0.721 respectively.
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References
Ostrum, Q., et al.: CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2008–2012. Neuro Oncol. 17, v1–v62 (2015)
Pati, S., et al.: The Federated Tumor Segmentation (FeTS) Challenge. arXiv:2105.05874 (2021)
Bonawitz, K., et al.: Towards Federated Learning at Scale: System Design. arXiv:1902.01046 (2019)
McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282. PMLR (2017)
Yang, T., et al.: Applied Federated Learning: Improving Google Keyboard Query Suggestions. arXiv:1812.02903 (2018)
Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. 10, 1–19 (2019)
Rieke, N., et al.: The future of digital health with federated learning. NPJ Digit. Med. 3, 1–7 (2020)
Sheller, M.J., et al.: Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci. Rep. 10, 1–12 (2020)
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). vol. 34, pp. 1993–2024 (2014)
Isensee, F., et al.: Abstract: nnU-Net: self-adapting framework for u-net-based medical image segmentation. In: Handels, H., Deserno, T.M., Maier, A., Maier-Hein, K.H., Palm, C., Tolxdorff, T. (eds.) Bildverarbeitung für die Medizin 2019: Algorithmen – Systeme – Anwendungen. Proceedings des Workshops vom 17. bis 19. März 2019 in Lübeck, pp. 22–22. Springer Fachmedien Wiesbaden, Wiesbaden (2019). https://doi.org/10.1007/978-3-658-25326-4_7
Kamnitsas, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv:1810.04805 (2018)
Dosovitskiy, A., et al.: An Image is Worth 16 × 16 Words: Transformers for Image Recognition at Scale. arXiv:2010.11929 (2020)
Wang, H., Zhu, Y., Green, B., Adam, H., Yuille, A., Chen, L.-C.: Axial-deeplab: stand-alone axial-attention for panoptic segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part IV, pp. 108–126. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-58548-8_7
Reina, G.A., et al.: OpenFL: An Open-source Framework for Federated Learning. arXiv:2105.06413 (2021)
Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge (2018)
Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017)
Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. The Cancer Imaging Archive 286 (2017)
Bakas, S., Akbari, H., Sotiras, A.: Segmentation labels for the pre-operative scans of the TCGA-GBM collection. The Cancer Imaging Archive. (2017)
Davatzikos, C., et al.: Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome. J. Med. Imaging 5, 011018 (2018)
Pati, S., et al.: The cancer imaging phenomics toolkit (captk): technical overview. In: Crimi, A., Bakas, S. (eds.) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part II, pp. 380–394. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-46643-5_38
Rathore, S., et al.: Brain cancer imaging phenomics toolkit (brain-CaPTk): an interactive platform for quantitative analysis of glioblastoma. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, pp. 133–145. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_12
Rohlfing, T., Zahr, N.M., Sullivan, E.V., Pfefferbaum, A.: The SRI24 multichannel atlas of normal adult human brain structure. Hum. Brain Mapp. 31, 798–819 (2010)
Yushkevich, P.A., Pluta, J., Wang, H., Wisse, L.E., Das, S., Wolk, D.: Fast automatic segmentation of hippocampal subfields and medial temporal lobe subregions in 3 Tesla and 7 Tesla T2-weighted MRI. Alzheimers Dement. 7, P126–P127 (2016)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III, pp. 234–241. Springer International Publishing, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Thakur, S., et al.: Brain extraction on MRI scans in presence of diffuse glioma: multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training. Neuroimage 220, 117081 (2020)
Bangalore Yogananda, C.G., et al.: A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas. Neuro Oncol. 22, 402–411 (2020)
Murugesan, G.K., et al.: Multidimensional and multiresolution ensemble networks for brain tumor segmentation. In: Crimi, A., Bakas, S. (eds.) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part II, pp. 148–157. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-46643-5_14
Nalawade, S.S., et al.: Brain Tumor IDH, 1p/19q, and MGMT Molecular Classification Using MRI-based Deep Learning: Effect of Motion and Motion Correction. bioRxiv (2020)
Nalawade, S., et al.: Classification of brain tumor isocitrate dehydrogenase status using MRI and deep learning. J. Med. Imaging 6(1–13), 13 (2019)
Valanarasu, J.M.J., Oza, P., Hacihaliloglu, I., Patel, V.M.: Medical transformer: gated axial-attention for medical image segmentation. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 36–46. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87193-2_4
Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Salehi, S.S.M., Erdogmus, D., Gholipour, A.: Tversky loss function for image segmentation using 3D fully convolutional deep networks. In: Wang, Q., Shi, Y., Suk, Heung-Il., Suzuki, K. (eds.) machine Learning in Medical Imaging, pp. 379–387. Springer International Publishing, Cham (2017). https://doi.org/10.1007/978-3-319-67389-9_44
Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J.: Comparing images using the Hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 15, 850–863 (1993)
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Nalawade, S. et al. (2022). Federated Learning for Brain Tumor Segmentation Using MRI and Transformers. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12963. Springer, Cham. https://doi.org/10.1007/978-3-031-09002-8_39
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