Multi-institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation
Open accessAbstract
Deep learning models for semantic segmentation of images require large amounts of data. In the medical imaging domain, acquiring sufficient data is a significant challenge. Labeling medical image data requires expert knowledge. Collaboration between institutions could address this challenge, but sharing medical data to a centralized location faces various legal, privacy, technical, and data-ownership challenges, especially among international institutions. In this study, we introduce the first use of federated learning for multi-institutional collaboration, enabling deep learning modeling without sharing patient data. Our quantitative results demonstrate that the performance of federated semantic segmentation models (Dice=0.852) on multimodal brain scans is similar to that of models trained by sharing data (Dice=0.862). We compare federated learning with two alternative collaborative learning methods and find that they fail to match the performance of federated learning.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Medical data is particularly diverse, and inhomogeneous data distribution poses a challenge for federated learning algorithms and strategies, as many assume independently and identically distributed data; strategies such as FedAvg are prone to fail under these conditions, but recent results indicate that FL training is still feasible even if medical data is not uniformly distributed or includes a
“Medical data is particularly diverse—not only because of the variety of modalities, dimensionality and characteristics in general, but even within a specific protocol due to factors such as acquisition differences, brand of the medical device or local demographics.”
From The future of digital health with federated learning (2020) · cites this paper as [17]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredModels trained by federated learning can achieve performance levels comparable to ones trained on centrally hosted data sets and superior to models that only see isolated single-institutional data.
“Recent research has shown that models trained by FL can achieve performance levels comparable to ones trained on centrally hosted data sets and superior to models that only see isolated single-institutional data16,17.”
From The future of digital health with federated learning (2020) · cites this paper as [17]
The Conclusions (page unavailable) report that the federated brain-tumor segmentation model achieved 99% of the performance of a data-sharing model, supporting the comparison with centralized training. The Experimental Results compare federated learning with other collaborative methods, not models trained solely on isolated single-institution data. The paper anticipates a benefit from adding another institution’s data but does not establish the claimed superiority over isolated models. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe federated learning workflow used in the present study, based on an aggregation server, had almost identical performance to centralized learning for detecting glioblastoma sub-compartment boundaries.
“Findings from these studies supported the superiority of the FL workflow used in the present study (i.e., based on an aggregation server10,14), which had almost identical performance to CL, for this use-case.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [15]
Federated learning provides an alternative paradigm for accurate and generalizable machine learning by only sharing numerical model updates from decentralized data.
“Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [15]
FL has made e-health services more accessible by allowing machine learning modeling despite the absence of health data.
“FL, for instance, has made e-health services more accessible by allowing machine learning (ML) modeling despite the absence of health data [10].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [10]
A general FL-trained model can achieve performance levels comparable to models trained on centrally hosted datasets and superior to models that only see isolated single-institutional data.
“Independently of the FL training strategy, a recent analysis for the medical field has shown that a general FL-trained model can achieve performance levels comparable to the ones trained on centrally hosted datasets and superior to models that only see isolated single-institutional data [27], [28].”
From Handling privacy-sensitive medical data with federated learning: Challenges and future directions (2023) · cites this paper as [28]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1611835114 · Cited by 7,899 · see citing works
The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781 · see citing works
Deep Learning with Differential Privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978318 · Cited by 6,578 · see citing works
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017) · Scientific Data · doi:10.1038/sdata.2017.117 · Cited by 3,049 · see citing works
Self-citationPrivacy-preserving deep learning
Shokri, R., & Shmatikov, V. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813687 · Cited by 2,356 · see citing works
Catastrophic forgetting in connectionist networks
French, R. (1999) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(99)01294-2 · Cited by 2,311 · see citing works
Federated Learning with Non-IID Data
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018) · arXiv · doi:10.48550/arxiv.1806.00582 · Cited by 2,077 · see citing works
How to backdoor federated learning
Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., & Shmatikov, V. (2018) · arXiv · doi:10.48550/arxiv.1807.00459 · Cited by 1,495 · see citing works
Differentially Private Federated Learning: A Client Level Perspective
Geyer, R. C., Klein, T., & Nabi, M. (2017) · arXiv · doi:10.48550/arxiv.1712.07557 · Cited by 1,052 · see citing works
Learning Differentially Private Recurrent Language Models
McMahan, H. B., Ramage, D., Talwar, K., & Zhang, L. (2017) · arXiv · doi:10.48550/arxiv.1710.06963 · Cited by 849 · see citing works
A deep learning model integrating FCNNs and CRFs for brain tumor segmentation
Zhao, X., Wu, Y., Song, G., Li, Z., Zhang, Y., & Fan, Y. (2018) · Medical Image Analysis · doi:10.1016/j.media.2017.10.002 · Cited by 813 · see citing works
Chang, K., Bai, H. X., Zhou, H., Su, C., Bi, W. L., Agbodza, E., Kavouridis, V. K., Senders, J. T., Boaro, A., Beers, A. L., et al. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-2236 · Cited by 392 · see citing works
Distributed deep learning networks among institutions for medical imaging
Chang, K., Balachandar, N., Lam, C., Yi, D., Brown, J., Beers, A., Rosen, B., Rubin, D. L., & Kalpathy-Cramer, J. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy017 · Cited by 340 · see citing works
Segmentation Labels for the Pre-operative Scans of the TCGA-GBM collection
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., & Davatzikos, C. (2017) · The Cancer Imaging Archive · doi:10.7937/k9/tcia.2017.klxwjj1q · Cited by 316 · see citing works
Self-citationMacyszyn, L. J., Akbari, H., Pisapia, J. M., Da, X., Attiah, M. A., Pigrish, V., Bi, Y., Pal, S., Davuluri, R. V., Roccograndi, L., et al. (2015) · Neuro-Oncology · doi:10.1093/neuonc/nov127 · Cited by 300 · see citing works
Segmentation Labels for the Pre-operative Scans of the TCGA-LGG collection
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., & Davatzikos, C. (2017) · The Cancer Imaging Archive · doi:10.7937/k9/tcia.2017.gjq7r0ef · Cited by 290 · see citing works
Self-citationResidual Deep Convolutional Neural Network Predicts MGMT Methylation Status
Korfiatis, P. D., Kline, T. L., Lachance, D. H., Parney, I. F., Buckner, J. C., & Erickson, B. J. (2017) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-017-0009-z · Cited by 200 · see citing works
Binder, Z. A., Thorne, A. H., Bakas, S., Wileyto, E. P., Bilello, M., Akbari, H., Rathore, S., Ha, S. M., Zhang, L., Ferguson, C., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.06.006 · Cited by 166 · see citing works
Self-citationAkbari, H., Macyszyn, L. J., Da, X., Bilello, M., Wolf, R. L., Martinez‐Lage, M., Biros, G., Alonso‐Basanta, M., O’Rourke, D. M., & Davatzikos, C. A. (2016) · Neurosurgery · doi:10.1227/neu.0000000000001202 · Cited by 164 · see citing works
Going Digital: A Survey on Digitalization and Large-Scale Data Analytics in Healthcare
Tresp, V., Overhage, J. M., Bundschus, M., Rabizadeh, S., Fasching, P. A., & Yu, S. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2016.2615052 · Cited by 154 · see citing works
Privacy Protection and Intrusion Avoidance for Cloudlet-Based Medical Data Sharing
Chen, M., Qian, Y., Chen, J., Hwang, K., Mao, S., & Hu, L. (2016) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2016.2617382 · Cited by 111 · see citing works
Bakas, S., Akbari, H., Pisapia, J. M., Martinez‐Lage, M., Rozycki, M., Rathore, S., Dahmane, N., O’Rourke, D. M., & Davatzikos, C. A. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-16-1871 · Cited by 89 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- [12]Brendan McMahan H, Moore E, Ramage D, Hampson S, Agera y Arcas B: Communication-E cient Learning of Deep Networks from Decentralized Data. ArXiv e-prints, (2016)
Cited by
Sorted by citations
Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2975749 · Cited by 6,210 · see citing works
The future of digital health with federated learning
Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00323-1 · Cited by 3,119 · see citing works
“Recent research has shown that this approach can achieve a performance that is comparable to a scenario where the data was co-located in a data lake and superior to the models that only see isolated singleinstitutional data [12, 13].”
BackgroundResultInfluentialSelf-citationSheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-69250-1 · Cited by 1,646 · see citing works
Self-citationFederated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Vincent Poor, H. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3075439 · Cited by 1,503 · see citing works
The Liver Tumor Segmentation Benchmark (LiTS)
Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., et al. (2023) · Medical Image Analysis · doi:10.1016/j.media.2022.102680 · Cited by 1,412 · see citing works
Self-citationAbdulrahman, S., Tout, H., Ould-Slimane, H., Mourad, A., Talhi, C., & Guizani, M. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3030072 · Cited by 796 · see citing works
Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications
Aledhari, M., Razzak, R., Parizi, R. M., & Saeed, F. (2020) · IEEE Access · doi:10.1109/access.2020.3013541 · Cited by 733 · see citing works
Federated learning for healthcare: Systematic review and architecture proposal
Antunes, R. S., André da Costa, C., Küderle, A., Yari, I. A., & Eskofier, B. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3501813 · Cited by 595 · see citing works
Privacy-Preserving Federated Brain Tumour Segmentation
Li, W., Milletarì, F., Xu, D., Rieke, N., Hancox, J., Zhu, W., Baust, M., Cheng, Y., Ourselin, S., Cardoso, M. J., et al. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32692-0_16 · Cited by 578 · see citing works
End-to-end privacy preserving deep learning on multi-institutional medical imaging
Kaissis, G., Ziller, A., Passerat-Palmbach, J., Ryffel, T., Usynin, D., Trask, A., Lima, I., Mancuso, J., Jungmann, F., Steinborn, M.-M., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00337-8 · Cited by 513 · see citing works
Federated learning for medical image analysis: A survey
Guan, H., Yap, P.-T., Bozoki, A., & Liu, M. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110424 · Cited by 518 · see citing works
Li, X., Gu, Y., Dvornek, N., Staib, L. H., Ventola, P., & Duncan, J. S. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101765 · Cited by 480 · see citing works
Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning
Fang, H., & Qian, Q. (2021) · Future Internet · doi:10.3390/fi13040094 · Cited by 424 · see citing works
Federated learning enables big data for rare cancer boundary detection
Pati, S., Baid, U., Edwards, B., Sheller, M., Wang, S.-H., Reina, G. A., Foley, P., Gruzdev, A., Karkada, D., Davatzikos, C., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33407-5 · Cited by 394 · see citing works
Self-citationRahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., Khan, M. S. I., Tiwari, P., & Band, S. S. (2023) · Cluster Computing · doi:10.1007/s10586-022-03658-4 · Cited by 399 · see citing works
Langlotz, C. P., Allen, B., Erickson, B. J., Kalpathy-Cramer, J., Bigelow, K., Cook, T. S., Flanders, A. E., Lungren, M. P., Mendelson, D. S., Rudie, J. D., et al. (2019) · Radiology · doi:10.1148/radiol.2019190613 · Cited by 368 · see citing works
Dynamic-Fusion-Based Federated Learning for COVID-19 Detection
Zhang, W., Zhou, T., Lu, Q., Wang, X., Zhu, C., Sun, H., Wang, Z., Lo, S. K., & Wang, F.-Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3056185 · Cited by 329 · see citing works
Rauniyar, A., Hagos, D. H., Jha, D., Håkegård, J. E., Bagci, U., Rawat, D. B., & Vlassov, V. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3329061 · Cited by 315 · see citing works
Yang, D., Xu, Z., Li, W., Myronenko, A., Roth, H. R., Harmon, S., Xu, S., Turkbey, B., Turkbey, E., Wang, X., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101992 · Cited by 279 · see citing works
Federated learning for computational pathology on gigapixel whole slide images
Lu, M. Y., Chen, R. J., Kong, D., Lipkova, J., Singh, R., Williamson, D. F. K., Chen, T. Y., & Mahmood, F. (2022) · Medical Image Analysis · doi:10.1016/j.media.2021.102298 · Cited by 267 · see citing works
Teo, Z. L., Jin, L., Liu, N., Li, S., Miao, D., Zhang, X., Ng, W. Y., Tan, T. F., Lee, D. M., Chua, K. J., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101419 · Cited by 255 · see citing works
“1,6 FL has been shown to remain robusttomultipledatatypesincludingimaging,suchasmagnetic resonance imaging (MRI) for brain tumor segmentation 7 ; chest X-rayforCOVID-19clinicaloutcomeprediction 8 ;electronicmed-ical records for predicting hospitalization 9 ; colored photographs, such as skin…”
BackgroundOgier du Terrail, J., Leopold, A., Joly, C., Béguier, C., Andreux, M., Maussion, C., Schmauch, B., Tramel, E. W., Bendjebbar, E., Zaslavskiy, M., et al. (2023) · Nature Medicine · doi:10.1038/s41591-022-02155-w · Cited by 217 · see citing works
Federated Learning for Breast Density Classification: A Real-World Implementation
Roth, H. R., Chang, K., Singh, P., Neumark, N., Li, W., Gupta, V., Gupta, S., Qu, L., Ihsani, A., Bizzo, B. C., et al. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-60548-3_18 · Cited by 202 · see citing works
Federated benchmarking of medical artificial intelligence with MedPerf
Karargyris, A., Umeton, R., Sheller, M. J., Aristizabal, A., George, J., Wuest, A., Pati, S., Kassem, H., Zenk, M., Baid, U., et al. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00652-2 · Cited by 188 · see citing works
Self-citationGuo, P., Wang, P., Zhou, J., Jiang, S., & Patel, V. M. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00245 · Cited by 193 · see citing works
Handling privacy-sensitive medical data with federated learning: Challenges and future directions
Aouedi, O., Sacco, A., Piamrat, K., & Marchetto, G. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3185673 · Cited by 165 · see citing works
Local and Central Differential Privacy for Robustness and Privacy in Federated Learning
Naseri, M., Hayes, J., & De Cristofaro, E. (2022) · Proceedings 2022 Network and Distributed System Security Symposium · doi:10.14722/ndss.2022.23054 · Cited by 188 · see citing works
Ng, D., Lan, X., Yao, M. M.-S., Chan, W. P., & Feng, M. (2021) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-20-595 · Cited by 152 · see citing works
A comprehensive survey on federated learning techniques for healthcare informatics
Dasaradharami Reddy, K., & Gadekallu, T. R. (2023) · Computational Intelligence and Neuroscience · doi:10.1155/2023/8393990 · Cited by 126 · see citing works
Reviewing Federated Machine Learning and Its Use in Diseases Prediction
Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Sensors · doi:10.3390/s23042112 · Cited by 114 · see citing works
Memory-aware curriculum federated learning for breast cancer classification
Jiménez-Sánchez, A., Tardy, M., González Ballester, M. A., Mateus, D., & Piella, G. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107318 · Cited by 114 · see citing works
Naeem, A., Anees, T., Naqvi, R. A., & Loh, W.-K. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12020275 · Cited by 103 · see citing works
Zerka, F., Urovi, V., Vaidyanathan, A., Barakat, S., Leijenaar, R. T. H., Walsh, S., Gabrani-Juma, H., Miraglio, B., Woodruff, H. C., Dumontier, M., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3029445 · Cited by 89 · see citing works
Thakur, S., Doshi, J., Pati, S., Rathore, S., Sako, C., Bilello, M., Ha, S. M., Shukla, G., Flanders, A., Kotrotsou, A., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117081 · Cited by 84 · see citing works
Self-citationFederated Learning for Privacy-Preserving Open Innovation Future on Digital Health
Long, G., Shen, T., Tan, Y., Gerrard, L., Clarke, A., & Jiang, J. (2022) · Humanity Driven AI · doi:10.1007/978-3-030-72188-6_6 · Cited by 85 · see citing works
Chain FL: Decentralized Federated Machine Learning via Blockchain
Korkmaz, C., Kocas, H. E., Uysal, A., Masry, A., Ozkasap, O., & Akgun, B. (2020) · 2020 Second International Conference on Blockchain Computing and Applications (BCCA) · doi:10.1109/bcca50787.2020.9274451 · Cited by 84 · see citing works
Artificial intelligence in glioma imaging: Challenges and advances
Jin, W., Fatehi, M., Abhishek, K., Mallya, M., Toyota, B., & Hamarneh, G. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab8131 · Cited by 55 · see citing works
Pati, S., Baid, U., Edwards, B., Sheller, M. J., Foley, P., Anthony Reina, G., Thakur, S., Sako, C., Bilello, M., Davatzikos, C., et al. (2022) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ac9449 · Cited by 51 · see citing works
Self-citationProtecting Personal Healthcare Record Using Blockchain & Federated Learning Technologies
Aich, S., Sinai, N. K., Kumar, S., Ali, M., Choi, Y. R., Joo, M.-I., & Kim, H.-C. (2022) · 2022 24th International Conference on Advanced Communication Technology (ICACT) · doi:10.23919/icact53585.2022.9728772 · Cited by 49 · see citing works
Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition
Yuan, L., Ma, Y., Su, L., & Wang, Z. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00553 · Cited by 45 · see citing works
Allen, B., Agarwal, S., Kalpathy-Cramer, J., & Dreyer, K. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2019.04.023 · Cited by 40 · see citing works
Bakas, S., Vollmuth, P., Galldiks, N., Booth, T. C., Aerts, H. J. W. L., Bi, W. L., Wiestler, B., Tiwari, P., Pati, S., Baid, U., et al. (2024) · The Lancet Oncology · doi:10.1016/s1470-2045(24)00315-2 · Cited by 36 · see citing works
Self-citationBayBFed: Bayesian Backdoor Defense for Federated Learning
Kumari, K., Rieger, P., Fereidooni, H., Jadliwala, M., & Sadeghi, A.-R. (2023) · 2023 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp46215.2023.10179362 · Cited by 37 · see citing works
A federated learning architecture for secure and private neuroimaging analysis
Stripelis, D., Gupta, U., Saleem, H., Dhinagar, N., Ghai, T., Anastasiou, C., Sánchez, R., Steeg, G. V., Ravi, S., Naveed, M., et al. (2024) · Patterns · doi:10.1016/j.patter.2024.101031 · Cited by 31 · see citing works
Multi-task Federated Learning for Heterogeneous Pancreas Segmentation
Shen, C., Wang, P., Roth, H. R., Yang, D., Xu, D., Oda, M., Wang, W., Fuh, C.-S., Chen, P.-T., Liu, K.-L., et al. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-90874-4_10 · Cited by 36 · see citing works
The role of federated learning models in medical imaging
Kwak, L., & Bai, H. (2023) · Radiology Artificial Intelligence · doi:10.1148/ryai.230136 · Cited by 25 · see citing works
An adaptive robust defending algorithm against backdoor attacks in federated learning
Wang, Y., Zhai, D.-H., He, Y., & Xia, Y. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.01.026 · Cited by 25 · see citing works
Scaling Neuroscience Research Using Federated Learning
Stripelis, D., Ambite, J. L., Lam, P., & Thompson, P. (2021) · 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9433925 · Cited by 29 · see citing works
Weimann, K., & Conrad, T. O. F. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3427787 · Cited by 19 · see citing works
Have You Forgotten? A Method to Assess if Machine Learning Models Have Forgotten Data
Liu, X., & Tsaftaris, S. A. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-59710-8_10 · Cited by 18 · see citing works
- Authors
- Sheller, M. J., Reina, G. A., Edwards, B., Martin, J., & Bakas, S.
- Affiliations
- Intel (United States) (US)
- University of Pennsylvania (US)
- Corresponding author
- Sheller, Micah J.
- Bakas, Spyridon
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Volume
- 11383
- Pages
- 92-104
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- PMC
- PMC6589345
- OpenAlex
- W2897230576
- Cited by
- 609
- References
- 23
- Citations by year
- 2026: 73 · 2025: 97 · 2024: 83 · 2023: 98 · 2022: 113 · 2021: 77 · 2020: 52 · 2019: 15 · 2018: 1
- FWCI
- 24.59
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (1s10od023495-01)
- National Cancer Institute (U24CA189523)
- National Institute of Neurological Disorders and Stroke (R01NS042645)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- federated learning, brain tumor segmentation, semantic segmentation, multi-institutional collaboration, data privacy, health data sharing, collaborative deep learning, data ownership
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
