Federated Learning for Computer-Aided Diagnosis of Glaucoma Using Retinal Fundus Images
Source abstract
Abstract
Deep learning approaches require a large amount of data to be transferred to centralized entities. However, this is often not a feasible option in healthcare, as it raises privacy concerns over sharing sensitive information. Federated Learning (FL) aims to address this issue by allowing machine learning without transferring the data to a centralized entity. FL has shown great potential to ensure privacy in digital healthcare while maintaining performance. Despite this, there is a lack of research on the impact of different types of data heterogeneity on the results. In this study, we research the robustness of various FL strategies on different data distributions and data quality for glaucoma diagnosis using retinal fundus images. We use RetinaQualEvaluator to generate quality labels for the datasets and then a data distributor to achieve our desired distributions. Finally, we evaluate the performance of the different strategies on local data and an independent test dataset. We observe that federated learning shows the potential to enable high-performance models without compromising sensitive data. Furthermore, we infer that FedProx is more suitable to scenarios where the distributions and quality of the data of the participating clients is diverse with less communication cost.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Retinal Imaging and Analysis · Privacy-Preserving Technologies in Data · AI in cancer detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Fraunhofer Portugal AICOS, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal
Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal
First Solutions—Sistemas de Informação S.A., Rua Conselheiro Costa Braga, 502F, 4450-102 Matosinhos, Portugal
Funding
the project Transparent Artificial Medical Intelligence (TAMI)
References (34)
Show source-provided references
1. Magadza, T., and Viriri, S. (2021). Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art. J. Imaging, 7.10.3390/jimaging7020019 ↗
2. Serte, S., and Demirel, H. (2021). Deep learning for diagnosis of COVID-19 using 3D CT scans. Comput. Biol. Med., 132.10.1016/j.compbiomed.2021.104306 ↗
3. Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation10.1148/radiol.2018180694 ↗
4. Deep Learning in Medical Ultrasound Analysis: A Review10.1016/j.eng.2018.11.020 ↗
5. Teleophthalmology: A Model for Eye Care Delivery in Rural and Underserved Areas of India
6. Yuan, B., and Li, J. (2019). The Policy Effect of the General Data Protection Regulation (GDPR) on the Digital Public Health Sector in the European Union: An Empirical Investigation. Int. J. Environ. Res. Public Health, 16.10.3390/ijerph16061070 ↗
7. Estimating the success of re-identifications in incomplete datasets using generative models10.1038/s41467-019-10933-3 ↗
8. McMahan, H.B., Ramage, D., Talwar, K., and Zhang, L. (2018). Learning Differentially Private Recurrent Language Models. arXiv.
9. McMahan, H.B., Moore, E., Ramage, D., Hampson, S., and Arcas, B.A.Y. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. arXiv.
10. Federated Learning for Breast Density Classification: A Real-World Implementation
11. Ho, T.T., Tran, K.D., and Huang, Y. (2022). FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information. Sensors, 22.10.3390/s22103728 ↗
12. Li, W., Milletarì, F., Xu, D., Rieke, N., Hancox, J., Zhu, W., Baust, M., Cheng, Y., Ourselin, S., and Cardoso, M.J. (2019). Privacy-preserving Federated Brain Tumour Segmentation. arXiv.10.1007/978-3-030-32692-0_16 ↗
13. Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation
14. Federated Learning for Microvasculature Segmentation and Diabetic Retinopathy Classification of OCT Data10.1016/j.xops.2021.100069 ↗
15. IoT-Based Federated Learning Model for Hypertensive Retinopathy Lesions Classification10.1109/TCSS.2022.3213507 ↗
16. Antony, B., Fu, H., Lee, C.S., MacGillivray, T., Xu, Y., and Zheng, Y. (2022). Ophthalmic Medical Image Analysis, Springer. Lecture Notes in Computer Science.10.1007/978-3-031-16525-2 ↗
17. Federated Learning for Multicenter Collaboration in Ophthalmology: Improving Classification Performance in Retinopathy of Prematurity10.1016/j.oret.2022.02.015 ↗
18. Federated Learning for Multicenter Collaboration in Ophthalmology: Implications for Clinical Diagnosis and Disease Epidemiology10.1016/j.oret.2022.03.005 ↗
19. Zhang, Z., Yin, F.S., Liu, J., Wong, W.K., Tan, N.M., Lee, B.H., Cheng, J., and Wong, T.Y. (September, January 31). ORIGA-light: An online retinal fundus image database for glaucoma analysis and research. Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC’10, Buenos Aires, Argentina.
20. REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs10.1016/j.media.2019.101570 ↗
21. Wu, J., Fang, H., Li, F., Fu, H., Lin, F., Li, J., Huang, L., Yu, Q., Song, S., and Xu, X. (2022). GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges. arXiv.10.1016/j.media.2023.102938 ↗
22. Sivaswamy, J., Krishnadas, S.R., Datt Joshi, G., Jain, M., and Syed Tabish, A.U. (May, January 29). Drishti-GS: Retinal image dataset for optic nerve head(ONH) segmentation. Proceedings of the 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), Beijing, China.10.1109/ISBI.2014.6867807 ↗
23. de Vente, C., Vermeer, K.A., Jaccard, N., Wang, H., Sun, H., Khader, F., Truhn, D., Aimyshev, T., Zhanibekuly, Y., and Le, T.D. (2023). AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge. arXiv.10.1109/TMI.2023.3313786 ↗
24. Impact of Generative Modeling for Fundus Image Augmentation With Improved and Degraded Quality in the Classification of Glaucoma10.1109/ACCESS.2022.3215126 ↗
25. Proportion of Glaucoma among Voluntary People Coming for Glaucoma Screening Program at Jimma University Department of Ophthalmology, Jimma, Ethiopia
26. Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V. (2020). Federated Optimization in Heterogeneous Networks. arXiv.
27. Li, X., Jiang, M., Zhang, X., Kamp, M., and Dou, Q. (2021). FedBN: Federated Learning on Non-IID Features via Local Batch Normalization. arXiv.
28. Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečný, J., Kumar, S., and McMahan, H.B. (2021). Adaptive Federated Optimization. arXiv.
29. Xia, Y., Yang, D., Li, W., Myronenko, A., Xu, D., Obinata, H., Mori, H., An, P., Harmon, S., and Turkbey, E. (2021). Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation. arXiv.
30. Guo, P., Yang, D., Hatamizadeh, A., Xu, A., Xu, Z., Li, W., Zhao, C., Xu, D., Harmon, S., and Turkbey, E. (2022). Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation. arXiv.10.1007/978-3-031-19803-8_26 ↗
31. Gunesli, G.N., Bilal, M., Raza, S.E.A., and Rajpoot, N.M. (2021). FedDropoutAvg: Generalizable federated learning for histopathology image classification. arXiv.
32. Jiang, M., Yang, H., Cheng, C., and Dou, Q. (2022). IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation. arXiv.10.1109/TMI.2023.3263072 ↗
33. Liu, Q., Chen, C., Qin, J., Dou, Q., and Heng, P.A. (2021). FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space. arXiv.10.1109/CVPR46437.2021.00107 ↗
34. Roth, H.R., Cheng, Y., Wen, Y., Yang, I., Xu, Z., Hsieh, Y.T., Kersten, K., Harouni, A., Zhao, C., and Lu, K. (2022). NVIDIA FLARE: Federated Learning from Simulation to Real-World. arXiv.