Abstract
Federated learning is a promising strategy for performing privacy-preserving, distributed learning for medical image segmentation. However, the data-level heterogeneity as well as system-level heterogeneity makes it challenging to optimize. In this paper, we propose to improve Federated optimization via local Contrastive learning and Global Process-aware Aggregation (referred as FedContrast-GPA), aiming to jointly address both data-level and system-level heterogeneity issues. In specific, To address data-level heterogeneity, we propose to learn a unified latent feature space via an intra-client and inter-client local prototype based contrastive learning scheme. Among which, intra-client contrastive learning is adopted to improve the discriminative ability of learned feature embedding at each client, while inter-client contrastive learning is introduced to achieve cross-client distribution perception and alignment in a privacy preserving manner. To address system-level heterogeneity, we further propose a simple yet effective process-aware aggregation scheme to achieve effective straggler mitigation. Experimental results on six prostate segmentation datasets demonstrate large performance boost over existing state-of-the-art methods.
This study was partially supported by the Natural Science Foundation of China via project U20A20199 and 62201341.
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References
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)
Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. Proc. Mach. Learn. Syst. 2, 429–450 (2020)
Karimireddy, S.P., Kale, S., Mohri, M., Reddi, S., Stich, S., Suresh, A.T.: SCAFFOLD: stochastic controlled averaging for federated learning. In: International Conference on Machine Learning, pp. 5132–5143. PMLR (2020)
Li, Q., He, B., Song, D.: Model-contrastive federated learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10713–10722) (2021)
Mendieta, M., Yang, T., Wang, P., Lee, M., Ding, Z., Chen, C.: Local learning matters: rethinking data-level heterogeneity in federated learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8397–8406 (2022)
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: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1013–1023 (2021)
Ouyang, C., Biffi, C., Chen, C., Kart, T., Qiu, H., Rueckert, D.: Self-supervision with Superpixels: training few-shot medical image segmentation without annotation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12374, pp. 762–780. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58526-6_45
Zhou, T., Wang, W., Konukoglu, E., Van Gool, L.: Rethinking semantic segmentation: a prototype view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2582–2593 (2022)
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607. PMLR (2020)
Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., Khazaeni, Y.: Federated learning with matched averaging. arXiv preprint arXiv:2002.06440 (2020)
Hsu, T.M.H., Qi, H., Brown, M.: Measuring the effects of non-identical data distribution for federated visual classification. arXiv preprint arXiv:1909.06335 (2019)
Wang, J., Liu, Q., Liang, H., Joshi, G., Poor, H.V.: Tackling the objective inconsistency problem in heterogeneous federated optimization. Adv. Neural Inf. Process. Syst. 33, 7611–7623 (2020)
Wu, Y., Zeng, D., Wang, Z., Shi, Y., Hu, J.: Federated contrastive learning for volumetric medical image segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12903, pp. 367–377. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87199-4_35
Dong, N., Xing, E.P.: Few-shot semantic segmentation with prototype learning. In: British Machine Vision Conference (BMVC). vol. 3, no. 4 (2018)
Liu, J., Qin, Y.: Prototype refinement network for few-shot segmentation. arXiv preprint arXiv:2002.03579 (2020)
Liu, Y., Zhang, X., Zhang, S., He, X.: Part-aware prototype network for few-shot semantic segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 142–158. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58545-7_9
Yu, Q., Dang, K., Tajbakhsh, N., Terzopoulos, D., Ding, X.: A location-sensitive local prototype network for few-shot medical image segmentation. In: IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 262–266. IEEE (2021)
Jaiswal, A., Babu, A.R., Zadeh, M.Z., Banerjee, D., Makedon, F.: A survey on contrastive self-supervised learning. Technologies 9(1), 2 (2020)
Liu, W., Wu, Z., Ding, H., Liu, F., Lin, J. and Lin, G.: Few-shot segmentation with global and local contrastive learning. arXiv preprint arXiv:2108.05293 (2021)
Chaitanya, K., Erdil, E., Karani, N., Konukoglu, E.: Contrastive learning of global and local features for medical image segmentation with limited annotations. Adv. Neural Inf. Process. Syst. 33, 12546–12558 (2020)
Zeng, D., et al.: Positional contrastive learning for volumetric medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 221–230. Springer (2021)
Wu, Y., Zeng, D., Wang, Z., Shi, Y., Hu, J.: Distributed contrastive learning for medical image segmentation. Med. Image Anal. 81, 102564 (2022)
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.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Bloch, N., Madabhushi, A., Huisman, H., Freymann, J., et al.: NCI-ISBI 2013 Challenge: Automated Segmentation of Prostate Structures (2015)
Lemaitre, G., Marti, R., Freixenet, J., Vilanova. J. C., et al.: Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: a review. In: Computers in Biology and Medicine. vol. 60, pp. 8–31 (2015)
Litjens, G., Toth, R., Ven, W., Hoeks, C., et al.: Evaluation of prostate segmentation algorithms for MRI: the promise12 challenge. In: Medical Image Analysis. vol. 18, pp. 359–373 (2014)
Liang, P.P., et al.: Think locally, act globally: federated learning with local and global representations. In: Workshop on Federated Learning at Advances in Neural Information Processing Systems. vol. 32 (2019)
Liu, Q., Dou, Q., Yu, L., Heng, P.A.: MS-Net: multi-site network for improving prostate segmentation with heterogeneous MRI data. IEEE Trans. Med. Imaging 39(9), 2713–2724 (2020)
Patro, S., Sahu, K.K.: Normalization: A preprocessing stage. arXiv preprint arXiv:1503.06462 (2015)
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Zhou, Q., Zheng, G. (2023). FedContrast-GPA: Heterogeneous Federated Optimization via Local Contrastive Learning and Global Process-Aware Aggregation. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14221. Springer, Cham. https://doi.org/10.1007/978-3-031-43895-0_62
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