Abstract
Federated learning, which trains a generic model for different institutions without sharing their data, is a new trend to avoid training with centralized data, which is often impossible due to privacy issues. The Federated Tumor Segmentation (FeTS) Challenge 2021 has two tasks for participants. Task 1 aims at effective weight aggregation methods given a pre-defined segmentation algorithm for clients training. While task 2 looks for robust segmentation algorithms evaluated on unseen data from remote independent institutions. In federated learning, heterogeneity in the local clients’ datasets and training speeds results in non-negligible variations between clients in each aggregation round. The naive weighted average aggregation of such models causes objective inconsistency. As for task 1, we devise a tensor normalization approach to solve the objective inconsistency. Furthermore, we propose a client pruning strategy to alleviate the negative impact on the convergence time caused by the uneven training time among local clients. Our method achieves a projected convergence score of 74.32% during the training phase. For task 2, we dynamically adapt model weights at test time by minimizing the entropy loss to address the domain shifting problem for unseen data evaluation. Our method finally achieves dice scores of 90.67%, 86.23%, and 78.90% for the whole tumor, tumor core, and enhancing tumor, respectively, on the task’s validation data. Overall, the proposed solution ranked first for task 2 and third for task 1 in the FeTS Challenge 2021.
The first two authors contributed equally. The work was done when they did summer internship at CUHK.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Pati, S., et al.: Reproducibility analysis of multi-institutional paired expert annotations and radiomic features of the Ivy glioblastoma atlas project (Ivy GAP) dataset. Med. Phys. 12, 6039–6052 (2020)
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)
Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge (2019)
Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017)
Isensee, F., Jäger, P.F., Full, P.M., Vollmuth, P., Maier-Hein, K.H.: nnU-Net for brain tumor segmentation. In: Crimi, A., Bakas, S. (eds.) BrainLes 2020. LNCS, vol. 12659, pp. 118–132. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72087-2_11
Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
Annas, G.J.: HIPAA regulations - a new era of medical-record privacy? N. Engl. J. Med. 348(15), 1486–1490 (2003)
McMahan, H.B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data (2017)
Yang, T., et al.: Applied federated learning: improving google keyboard query suggestions (2018)
Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. (TIST) 2, 1–19 (2019)
Rieke, N., et al.: The future of digital health with federated learning. NPJ Digital Med. 1, 1–7 (2020)
Sheller, M.J., et al.: Federated learning in medicine: facilitating multi-institutional pruning without sharing patient data. Sci. Rep. 1, 1–12 (2020)
Dou, Q., et al.: Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study. NPJ Digital Med. 4(1), 1–11 (2021)
Roth, H.R., et al.: Federated learning for breast density classification: a real-world implementation. In: Albarqouni, S., et al. (eds.) DART/DCL -2020. LNCS, vol. 12444, pp. 181–191. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60548-3_18
Li, W., et al.: Privacy-preserving federated brain tumour segmentation. In: Suk, H.-I., Liu, M., Yan, P., Lian, C. (eds.) MLMI 2019. LNCS, vol. 11861, pp. 133–141. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32692-0_16
Kaissis, G.A., Makowski, M.R., Rückert, D., Braren, R.F.: Secure, privacy-preserving and federated machine learning in medical imaging. Nat. Mach. Intell. 2(6), 305–311 (2020)
Li, D., Kar, A., Ravikumar, N., Frangi, A.F., Fidler, S.: Federated simulation for medical imaging. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 159–168. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59710-8_16
Sheller, M.J., Reina, G.A., Edwards, B., Martin, J., Bakas, S.: Multi-institutional deep learning modeling without sharing patient data: a feasibility study on brain tumor segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11383, pp. 92–104. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11723-8_9
Silva, S., Gutman, B.A., Romero, E., Thompson, P.M., Altmann, A., Lorenzi, M.: Federated learning in distributed medical databases: meta-analysis of large-scale subcortical brain data. In: ISBI, pp. 270–274. IEEE (2019)
Pati, S., et al.: The federated tumor segmentation (FeTS) challenge (2021)
Guo, P., Wang, P., Zhou, J., Jiang, S., Patel, V.M.: Multi-institutional collaborations for improving deep learning-based magnetic resonance image reconstruction using federated learning (2021)
Mohri, M., Sivek, G., Suresh, A.T.: Agnostic federated learning (2019)
Wang, J., Liu, Q., Liang, H., Joshi, G., Poor, H.V.: Tackling the objective inconsistency problem in heterogeneous federated optimization (2020)
Liu, Q., Chen, C., Qin, J., Dou, Q., Heng, P.-A.: FedDG: federated domain generalization on medical image segmentation via episodic learning in continuous frequency space. In: CVPR (2021)
Chen, C., Liu, Q., Jin, Y., Dou, Q., Heng, P.-A.: Source-free domain adaptive fundus image segmentation with denoised pseudo-labeling. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 225–235. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87240-3_22
Wang, D., Shelhamer, E., Liu, S., Olshausen, B., Darrell, T.: Tent: fully test-time adaptation by entropy minimization (2021)
Liu, C., Belkin, M.: Accelerating SGD with momentum for over-parameterized learning. arXiv preprint arXiv:1810.13395 (2018)
Karani, N., Erdil, E., Chaitanya, K., Konukoglu, E.: Test-time adaptable neural networks for robust medical image segmentation. Med. Image Anal. 68, 101907 (2021)
Sun, Y., Wang, X., Liu, Z., Miller, J., Efros, A., Hardt, M.: Test-time training with self-supervision for generalization under distribution shifts (2020)
Reina, G.A., et al.: OpenFL: an open-source framework for federated learning (2021)
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection (brats-TCGA-GBM). Cancer Imaging Archive (2017)
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection (brats-TCGA-LGG). Cancer Imaging Archive (2017)
Liu, Q., Yang, H., Dou, Q., Heng, P.-A.: Federated semi-supervised medical image classification via inter-client relation matching. arXiv preprint arXiv:2106.08600 (2021)
Acknowledgement
This work was supported by Key-Area Research and Development Program of Guangdong Province, China (2020B010165004), Hong Kong RGC TRS Project No. T42-409/18-R, National Natural Science Foundation of China with Project No. U1813204 and Shenzhen-HK Collaborative Development Zone.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yin, Y. et al. (2022). Efficient Federated Tumor Segmentation via Normalized Tensor Aggregation and Client Pruning. 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_38
Download citation
DOI: https://doi.org/10.1007/978-3-031-09002-8_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-09001-1
Online ISBN: 978-3-031-09002-8
eBook Packages: Computer ScienceComputer Science (R0)
