Abstract
Medical image segmentation under federated learning (FL) is a promising direction by allowing multiple clinical sites to collaboratively learn a global model without centralizing datasets. However, using a single model to adapt to various data distributions from different sites is extremely challenging. Personalized FL tackles this issue by only utilizing partial model parameters shared from global server, while keeping the rest to adapt to its own data distribution in the local training of each site. However, most existing methods concentrate on the partial parameter splitting, while do not consider the inter-site in-consistencies during the local training, which in fact can facilitate the knowledge communication over sites to benefit the model learning for improving the local accuracy. In this paper, we propose a personalized federated framework with Local Calibration (LC-Fed), to leverage the inter-site in-consistencies in both feature- and prediction- levels to boost the segmentation. Concretely, as each local site has its alternative attention on the various features, we first design the contrastive site embedding coupled with channel selection operation to calibrate the encoded features. Moreover, we propose to exploit the knowledge of prediction-level in-consistency to guide the personalized modeling on the ambiguous regions, e.g., anatomical boundaries. It is achieved by computing a disagreement-aware map to calibrate the prediction. Effectiveness of our method has been verified on three medical image segmentation tasks with different modalities, where our method consistently shows superior performance to the state-of-the-art personalized FL methods. Code is available at https://github.com/jcwang123/FedLC.
J. Wang and Y. Jin—Contributed equally.
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References
Andreux, M., du Terrail, J.O., Beguier, C., Tramel, E.W.: Siloed federated learning for multi-centric histopathology datasets. In: Albarqouni, S., et al. (eds.) DART/DCL -2020. LNCS, vol. 12444, pp. 129–139. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60548-3_13
Batista, F.J.F., Diaz-Aleman, T., Sigut, J., Alayon, S., Arnay, R., Angel-Pereira, D.: Rim-one dl: A unified retinal image database for assessing glaucoma using deep learning. Image Anal. Stereology 39(3), 161–167 (2020). https://doi.org/10.5566/ias.2346, https://www.ias-iss.org/ojs/IAS/article/view/2346
Bernal, J., Sánchez, F.J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., Vilariño, F.: WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput. Med. Imaging Graph. 43, 99–111 (2015)
Bernal, J., Sánchez, J., Vilarino, F.: Towards automatic polyp detection with a polyp appearance model. Pattern Recogn. 45(9), 3166–3182 (2012)
Bo, D., Wenhai, W., Deng-Ping, F., Jinpeng, L., Huazhu, F., Ling, S.: Polyp-pvt: polyp segmentation with pyramidvision transformers (2021)
Chen, Z., Zhu, M., Yang, C., Yuan, Y.: Personalized retrogress-resilient framework for real-world medical federated learning. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12903, pp. 347–356. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87199-4_33
Collins, L., Hassani, H., Mokhtari, A., Shakkottai, S.: Exploiting shared representations for personalized federated learning. arXiv preprint. arXiv:2102.07078 (2021)
Dinh, C.T., Vu, T.T., Tran, N.H., Dao, M.N., Zhang, H.: Fedu: a unified framework for federated multi-task learning with laplacian regularization. arXiv preprint. arXiv:2102.07148 (2021)
Dong, B., Wang, W., Fan, D.P., Li, J., Fu, H., Shao, L.: Polyp-pvt: polyp segmentation with pyramid vision transformers. arXiv preprint. arXiv:2108.06932 (2021)
Fallah, A., Mokhtari, A., Ozdaglar, A.: Personalized federated learning with theoretical guarantees: a model-agnostic meta-learning approach. In: Advances in Neural Information Processing Systems vol. 33, pp. 3557–3568 (2020)
Fan, D.P., Ji, G.P., Zhou, T., Chen, G., Fu, H., Shen, J., Shao, L.: Pranet: Parallel reverse attention network for polyp segmentation. In: International conference on medical image computing and computer-assisted intervention. pp. 263–273. Springer (2020)
Jha, D., Smedsrud, P.H., Riegler, M.A., Halvorsen, P., de Lange, T., Johansen, D., Johansen, H.D.: Kvasir-seg: A segmented polyp dataset. In: International Conference on Multimedia Modeling. pp. 451–462. Springer (2020)
Ji, W., Yu, S., Wu, J., Ma, K., Bian, C., Bi, Q., Li, J., Liu, H., Cheng, L., Zheng, Y.: Learning calibrated medical image segmentation via multi-rater agreement modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 12341–12351 (June 2021)
Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al.: Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977 (2019)
Kaissis, G.A., Makowski, M.R., Rückert, D., Braren, R.F.: Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence 2(6), 305–311 (2020)
Konečnỳ, J., McMahan, H.B., Yu, F.X., Richtárik, P., Suresh, A.T., Bacon, D.: Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016)
Kulkarni, V., Kulkarni, M., Pant, A.: Survey of personalization techniques for federated learning. In: 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). pp. 794–797. IEEE (2020)
Li, D., Kar, A., Ravikumar, N., Frangi, A.F., Fidler, S.: Federated simulation for medical imaging. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 159–168. Springer (2020)
Li, W., Milletarì, F., Xu, D., Rieke, N., Hancox, J., Zhu, W., Baust, M., Cheng, Y., Ourselin, S., Cardoso, M.J., et al.: Privacy-preserving federated brain tumour segmentation. In: International workshop on machine learning in medical imaging. pp. 133–141. Springer (2019)
Li, X., Jiang, M., Zhang, X., Kamp, M., Dou, Q.: Fed{bn}: Federated learning on non-{iid} features via local batch normalization. In: International Conference on Learning Representations (2021), https://openreview.net/pdf?id=6YEQUn0QICG
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)
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)
Marfoq, O., Neglia, G., Bellet, A., Kameni, L., Vidal, R.: Federated multi-task learning under a mixture of distributions. In: Advances in Neural Information Processing Systems, vol. 34 (2021)
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)
Orlando, J.I., et al.: Refuge challenge: a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Med. Image Anal. 59, 101570 (2020)
Reisizadeh, A., Mokhtari, A., Hassani, H., Jadbabaie, A., Pedarsani, R.: Fedpaq: a communication-efficient federated learning method with periodic averaging and quantization. In: International Conference on Artificial Intelligence and Statistics, pp. 2021–2031. PMLR (2020)
Rieke, N., et al.: The future of digital health with federated learning. NPJ Digit. Med. 3(1), 1–7 (2020)
Sarhan, A., et al.: Utilizing transfer learning and a customized loss function for optic disc segmentation from retinal images. In: Proceedings of the Asian Conference on Computer Vision (2020)
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, J., Histace, A., Romain, O., Dray, X., Granado, B.: Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer. Int. J. Comput. Assist. Radiol. Surg. 9(2), 283–293 (2013). https://doi.org/10.1007/s11548-013-0926-3
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: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 270–274. IEEE (2019)
Sivaswamy, J., Krishnadas, S., Chakravarty, A., Joshi, G., Tabish, A.S., et al.: A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis. JSM Biomed. Imaging Data Pap. 2(1), 1004 (2015)
Tan, A.Z., Yu, H., Cui, L., Yang, Q.: Towards personalized federated learning. IEEE Trans. Neural Networks Learn. Syst. (2022)
Thakur, N., Juneja, M.: Optic disc and optic cup segmentation from retinal images using hybrid approach. Expert Syst. Appl. 127, 308–322 (2019)
Tian, Z., Liu, L., Zhang, Z., Fei, B.: Psnet: prostate segmentation on MRI based on a convolutional neural network. J. Med. Imaging 5(2), 021208 (2018)
Wang, K., Mathews, R., Kiddon, C., Eichner, H., Beaufays, F., Ramage, D.: Federated evaluation of on-device personalization. arXiv preprint. arXiv:1910.10252 (2019)
Yu, T., Bagdasaryan, E., Shmatikov, V.: Salvaging federated learning by local adaptation. arXiv preprint. arXiv:2002.04758 (2020)
Zavala-Romero, O., et al.: Segmentation of prostate and prostate zones using deep learning. Strahlenther. Onkol. 196(10), 932–942 (2020). https://doi.org/10.1007/s00066-020-01607-x
Zhang, L., Lei, X., Shi, Y., Huang, H., Chen, C.: Federated learning with domain generalization. arXiv preprint. arXiv:2111.10487 (2021)
Zhang, Q.L., Yang, Y.B.: Sa-net: shuffle attention for deep convolutional neural networks. In: ICASSP 2021–2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2235–2239. IEEE (2021)
Zhu, W., Kairouz, P., McMahan, B., Sun, H., Li, W.: Federated heavy hitters discovery with differential privacy. In: International Conference on Artificial Intelligence and Statistics, pp. 3837–3847. PMLR (2020)
Acknowledgement
This work was supported by the Ministry of Science and Technology of the People’s Republic of China (2021ZD0201900)(2021ZD0201903).
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Wang, J., Jin, Y., Wang, L. (2022). Personalizing Federated Medical Image Segmentation via Local Calibration. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13681. Springer, Cham. https://doi.org/10.1007/978-3-031-19803-8_27
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