Federated learning in medical imaging: Part II: methods, challenges, and considerations
Open accessAbstract
Federated learning is a machine learning method that allows decentralized training of deep neural networks among multiple clients while preserving the privacy of each client's data. Federated learning is instrumental in medical imaging because of the privacy considerations of medical data. Setting up federated networks in hospitals comes with unique challenges, primarily because medical imaging data and federated learning algorithms each have their own set of distinct characteristics. This article introduces federated learning algorithms in medical imaging and discusses technical challenges and considerations of real-world implementation of them.
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?
UDA methods force the model to learn domain-agnostic features through adversarial learning or a specific type of batch normalization.
“UDA methods force the model to learn domain-agnostic features through adversarial learning [6] or a specific type of batch normalization [7].”
From Memory-aware curriculum federated learning for breast cancer classification (2023) · cites this paper as [7]
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
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215 · 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
Federated Optimization in Heterogeneous Networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2018) · arXiv · doi:10.48550/arxiv.1812.06127 · Cited by 4,023 · see citing works
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
Fredrikson, M., Jha, S., & Ristenpart, T. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813677 · Cited by 2,961 · see citing works
Sheller, 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
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Hitaj, B., Ateniese, G., & Perez-Cruz, F. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3134012 · Cited by 1,377 · see citing works
Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning
Wang, Z., Song, M., Zhang, Z., Song, Y., Wang, Q., & Qi, H. (2019) · IEEE INFOCOM 2019 - IEEE Conference on Computer Communications · doi:10.1109/infocom.2019.8737416 · Cited by 881 · see citing works
A cost-benefit analysis of electronic medical records in primary care
Wang, S. J., Middleton, B., Prosser, L. A., Bardon, C. G., Spurr, C. D., Carchidi, P. J., Kittler, A. F., Goldszer, R. C., Fairchild, D. G., Sussman, A. J., et al. (2003) · The American Journal of Medicine · doi:10.1016/s0002-9343(03)00057-3 · Cited by 740 · see citing works
IEEE INFOCOM 2019 - IEEE Conference on Computer Communications
(2019) · doi:10.1109/infocom40694.2019 · Cited by 732 · see citing works
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks
Carlini, N., Liu, C., Erlingsson, Ú., Kos, J., & Song, D. (2018) · arXiv · doi:10.48550/arxiv.1802.08232 · Cited by 629 · see citing works
Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss
Quan, T. M., Nguyen-Duc, T., & Jeong, W. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2820120 · Cited by 614 · see citing works
See through Gradients: Image Batch Recovery via GradInversion
Yin, H., Mallya, A., Vahdat, A., Álvarez, J. M., Kautz, J., & Molchanov, P. A. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01607 · Cited by 482 · see citing works
The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks
Zhang, Y., Jia, R., Pei, H., Wang, W., Li, B., & Song, D. X. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00033 · Cited by 481 · 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
(2014) · IEEE Pulse · doi:10.1109/mpul.2014.2316040 · Cited by 456 · see citing works
Li, M., Yu, S., Ren, K., & Lou, W. (2010) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-642-16161-2_6 · Cited by 401 · 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
A Performance Evaluation of Federated Learning Algorithms
Nilsson, A., Smith, S., Ulm, G., Gustavsson, E., & Jirstrand, M. (2018) · Proceedings of the Second Workshop on Distributed Infrastructures for Deep Learning · doi:10.1145/3286490.3286559 · Cited by 328 · see citing works
Dou, Q., So, T. Y., Jiang, M., Liu, Q., Vardhanabhuti, V., Kaissis, G., Li, Z., Si, W., Lee, H. H. C., Yu, K., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00431-6 · Cited by 278 · see citing works
Free DICOM de-identification tools in clinical research: functioning and safety of patient privacy
Aryanto, K. Y. E., Oudkerk, M., & van Ooijen, P. M. A. (2015) · European Radiology · doi:10.1007/s00330-015-3794-0 · Cited by 111 · see citing works
A distributed ensemble approach for mining healthcare data under privacy constraints
Li, Y., Bai, C., & Reddy, C. K. (2015) · Information Sciences · doi:10.1016/j.ins.2015.10.011 · Cited by 90 · see citing works
Pan, I., Thodberg, H. H., Halabi, S. S., Kalpathy–Cramer, J., & Larson, D. B. (2019) · Radiology Artificial Intelligence · doi:10.1148/ryai.2019190053 · Cited by 54 · see citing works
Xin, Y., Walia, E., & Babyn, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.03700 · Cited by 49 · see citing works
Privacy-Preserving Distributed Deep Learning for Clinical DataPreprint
Beaulieu‐Jones, B. K., Yuan, W., Finlayson, S. G., & Wu, Z. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.01484 · Cited by 38 · see citing works
Federated Learning on Distributed Medical Records for Detection of Lung Nodules
Baheti, P., Sikka, M., Arya, K. V., & Rajesh, R. (2020) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0009144704450451 · Cited by 35 · see citing works
Model poisoning attacks against distributed machine learning systems
Tomsett, R., Chan, K. S., & Chakraborty, S. (2019) · Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications · doi:10.1117/12.2520275 · Cited by 27 · see citing works
A machine learning methodology for medical imaging anonymization
Monteiro, E., Costa, C. M. A., & Oliveira, J. L. (2015) · doi:10.1109/embc.2015.7318626 · Cited by 13 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- Wang Z, Hu Q. Blockchain-based federated learning: a comprehensive survey. Preprint. arXiv 2021; 2110.02182.
- Li X, Jiang M, Zhang X, Kamp M, Dou Q. Fedbn: Federated learning on non-iid features via local batch normalization. Preprint. arXiv 2021; 2102.07623.
- Poirot MG, Vepakomma P, Chang K, Kalpathy-Cramer J, Gupta R, Raskar R. Split learning for collaborative deep learning in healthcare. Preprint. arXiv 2019; 1912.12115.
- Zhao Y, Li M, Lai L, Suda N, Civin D, Chandra V. Federated learning with non-iid data. Preprint. arXiv 2018; 1806.00582.
Cited by
Sorted by citations
Drukker, K., Chen, W., Gichoya, J. W., Gruszauskas, N. P., Kalpathy-Cramer, J., Koyejo, S., Myers, K. J., Sá, R. C., Sahiner, B., Whitney, H. M., et al. (2023) · Journal of Medical Imaging · doi:10.1117/1.jmi.10.6.061104 · Cited by 149 · 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
Federated learning for medical imaging radiology
Rehman, M. H. U., Pinaya, W. H. L., Nachev, P., Teo, J., Sebastien Ourselin, A., & Cardoso, M. J. (2023) · British Journal of Radiology · doi:10.1259/bjr.20220890 · Cited by 118 · see citing works
Federated Learning in Health care Using Structured Medical Data
Oh, W., & Nadkarni, G. N. (2023) · Advances in Kidney Disease and Health · doi:10.1053/j.akdh.2022.11.007 · Cited by 85 · see citing works
Yang, M., Huang, Y., Xu, C., Liu, C., & Dai, B. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124631 · Cited by 73 · see citing works
Mastoi, Q.-U.-A., Latif, S., Brohi, S. N., Ahmad, J. E., Alqhatani, A., Alshehri, M. S., Mazroa, A. A., & Ullah, R. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1535478 · Cited by 71 · see citing works
Federated Learning for Healthcare: A Comprehensive Review
Dhade, P., & Shirke, P. (2024) · RAiSE-2023 · doi:10.3390/engproc2023059230 · Cited by 67 · see citing works
Privacy-preserving Federated Learning and Uncertainty Quantification in Medical Imaging
Koutsoubis, N., Waqas, A., Yılmaz, Y., Ramachandran, R. P., Schabath, M. B., & Rasool, G. (2025) · Radiology Artificial Intelligence · doi:10.1148/ryai.240637 · Cited by 50 · see citing works
Walsh, G., Stogiannos, N., van de Venter, R., Rainey, C., Tam, W., McFadden, S. L., McNulty, J. P., Mekiš, N., Lewis, S. J., O’Regan, T., et al. (2023) · BJR|Open · doi:10.1259/bjro.20230033 · Cited by 47 · see citing works
Sourlos, N., Vliegenthart, R., Santinha, J., Klontzas, M. E., Cuocolo, R., Huisman, M., & van Ooijen, P. M. A. (2024) · Insights into Imaging · doi:10.1186/s13244-024-01833-2 · Cited by 44 · see citing works
Federated Learning in radiomics: A comprehensive meta-survey on medical image analysis
Raza, A., Guzzo, A., Ianni, M., Lappano, R., Zanolini, A., Maggiolini, M., & Fortino, G. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108768 · Cited by 42 · see citing works
Exploring Adversarial Attacks in Federated Learning for Medical Imaging
Darzi, E., Dubost, F., Sijtsema, N. M., & van Ooijen, P. M. A. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3423457 · Cited by 45 · see citing works
Linguraru, M. G., Bakas, S., Aboian, M., Chang, P., Flanders, A. E., Kalpathy–Cramer, J., Kitamura, F., Lungren, M. P., Mongan, J., Prevedello, L. M. S., et al. (2024) · Radiology Artificial Intelligence · doi:10.1148/ryai.240225 · Cited by 39 · see citing works
A comparative study of federated learning methods for COVID-19 detection
Darzidehkalani, E., Sijtsema, N. M., & van Ooijen, P. M. A. (2024) · Scientific Reports · doi:10.1038/s41598-024-54323-2 · Cited by 31 · see citing works
Federated and transfer learning for cancer detection based on image analysis
Bechar, A., Medjoudj, R., Elmir, Y., Himeur, Y., & Amira, A. (2025) · Neural Computing and Applications · doi:10.1007/s00521-024-10956-y · Cited by 36 · see citing works
Zhou, B., Miao, T., Mirian, N., Chen, X., Xie, H., Feng, Z., Guo, X., Li, X., Zhou, S. K., Duncan, J. S., et al. (2022) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2022.3194408 · Cited by 30 · see citing works
Real-world federated learning in radiology: hurdles to overcome and benefits to gain
Bujotzek, M., Akünal, Ü., Denner, S., Neher, P., Zenk, M., Frodl, E., Jaiswal, A., Kim, M., Krekiehn, N. R., Nickel, M., et al. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae259 · Cited by 25 · see citing works
Klontzas, M. E., Kalarakis, G., Koltsakis, E., Papathomas, T. G., Karantanas, A. H., & Tzortzakakis, A. (2024) · Insights into Imaging · doi:10.1186/s13244-023-01601-8 · Cited by 22 · see citing works
Tzortzis, I. N., Gutierrez-Torre, A., Sykiotis, S., Agulló, F., Bakalos, N., Doulamis, A. D., Doulamis, N. D., & Berral, J. L. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.03.031 · Cited by 22 · see citing works
Liu, Y., Huang, J., Chen, J., Chen, W., Pan, Y., & Qiu, J. (2024) · BMC Cancer · doi:10.1186/s12885-024-12456-7 · Cited by 21 · see citing works
Federated Learning for Diabetic Retinopathy Detection in a Multi-center Fundus Screening Network
Matta, S. C., Hassine, M. B., Lecat, C., Borderie, L., Guilcher, A. L., Massin, P., Cochener, B., Lamard, M., & Quellec, G. (2023) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc40787.2023.10340772 · Cited by 21 · see citing works
Automatic Adversarial Adaption for Stealthy Poisoning Attacks in Federated Learning
Krauß, T., König, J., Dmitrienko, A., & Kanzow, C. (2024) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.241366 · Cited by 20 · see citing works
Role of federated learning in healthcare systems: A survey
Rana, N., & Marwaha, H. (2023) · Mathematical Foundations of Computing · doi:10.3934/mfc.2023023 · Cited by 21 · see citing works
Xiang, H., Zhang, M., & Jian, C. (2023) · Cluster Computing · doi:10.1007/s10586-023-04143-2 · Cited by 18 · see citing works
Gupta, C., Gill, N. S., Gulia, P., Alduaiji, N., Shreyas, J., & Shukla, P. K. (2025) · Scientific Reports · doi:10.1038/s41598-024-80187-7 · Cited by 17 · see citing works
Evaluating artificial intelligence for medical imaging: a primer for clinicians
Keni, S. (2024) · British Journal of Hospital Medicine · doi:10.12968/hmed.2024.0312 · Cited by 15 · see citing works
Yang, Y., Cheng, J., Liu, Z., Li, H., & Xu, G. (2024) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-023-00584-7 · Cited by 14 · see citing works
Murala, D. K., Krishna, G. S., Kiran, T., & Mohamud, A. K. (2025) · Scientific Reports · doi:10.1038/s41598-025-27303-3 · Cited by 13 · see citing works
Nemane, S. G., Sarad, V. M., Tulaskar, D. P., Marode, T. P., Bhangdiya, V. K., Agrawal, L., Avthankar, A., Kondylakis, H., Kulkarni, M. B., Bhaiyya, M. L., et al. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10629-0 · Cited by 13 · see citing works
Federated learning and differential privacy in clinical health: Extensive survey
Odera, D. (2023) · World Journal of Advanced Engineering Technology and Sciences · doi:10.30574/wjaets.2023.8.2.0113 · Cited by 13 · see citing works
Yeom, J. C., Kim, J., Kim, Y. J., Kim, K. G., & Kim, K. G. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01020-1 · Cited by 12 · see citing works
An Investigation of Federated Learning Strategies for Disease Diagnosis
Das, A., Krishnadas, A., Krishnan, V. S., Farida, A., & Sarath, G. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10725147 · Cited by 11 · see citing works
Huang, Y., Song, Y., & Jing, Z. (2024) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2024.102724 · Cited by 10 · see citing works
Federated Learning: Challenges, SoTA, Performance Improvements and Application Domains
Schoinas, Ι., Triantafyllou, A., Ioannidis, D., Tzovaras, D. K., Drosou, A., Votis, K., Λάγκας, Θ., Argyriou, V., & Sarigiannidis, P. G. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3458088 · Cited by 10 · see citing works
Non-IID Medical Imaging Data on COVID-19 in the Federated Learning Framework: Impact and Directions
Al-Hafiz, F., & Basuhail, A. A. (2024) · COVID · doi:10.3390/covid4120140 · Cited by 10 · see citing works
Future of the Medical World: Collaborative Medical Imaging AI With Federated Learning
Rahmaniar, W., Deng, Z., Yang, Y., Jin, Z., & Suzuki, K. (2025) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2025.3539474 · Cited by 9 · see citing works
How to cope with malicious federated learning clients: An unsupervised learning-based approach
Önsü, M. A., Kantarcı, B., & Boukerche, A. (2023) · Computer Networks · doi:10.1016/j.comnet.2023.109938 · Cited by 8 · see citing works
Edge-Assisted Label-Flipping Attack Detection in Federated Learning
Alotaibi, N., Felemban, M., & Mahmood, S. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3496872 · Cited by 8 · see citing works
Healthcare federated learning: a survey of applications and frameworks
Pais, V., Rao, S., & Muniyal, B. (2025) · International Journal of Computers and Applications · doi:10.1080/1206212x.2025.2496913 · Cited by 8 · see citing works
Robust hierarchical federated learning with dual-layer filtering mechanism
Yang, C., Liang, W., Chen, Y., Xiao, J., He, D., Liu, T., Wang, J., & Tolba, A. K. R. (2025) · International Journal of Advanced Robotic Systems · doi:10.1177/17298806251322260 · Cited by 7 · see citing works
Szugye, N. A., Mahalingam, N., Somasundaram, E., Villa, C. R., Segala, J., Segala, M., Zafar, F., Morales, D. L. S., & Moore, R. A. (2024) · Pediatric Cardiology · doi:10.1007/s00246-024-03470-4 · Cited by 7 · see citing works
Securing Healthcare AI: Applied Federal Learning
Huda, M. N., Talukder, M. B., & Kumar, S. (2024) · Studies in systems, decision and control · doi:10.1007/978-3-031-75771-6_17 · Cited by 6 · see citing works
Robles‐Medranda, C., Verpalen, I. M., Schulz, D., & Spadaccini, M. (2025) · Gastroenterology · doi:10.1053/j.gastro.2025.04.011 · Cited by 6 · see citing works
Artificial Intelligence in digital pathology of breast cancer, new era of practice?
Li, W., Ye, S., Jin, Z., Chen, L., Chao, Y., Wei, G., Huang, Q., Tu, H., & Wang, Q. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000002953 · Cited by 7 · see citing works
TinyML and Federated Learning for Resource-Constrained Medical Devices
Fusco, P., Rimoli, G. P., & Ficco, M. (2025) · Engineering cyber-physical systems and critical infrastructures · doi:10.1007/978-3-031-70775-9_7 · Cited by 6 · see citing works
Collaborative Health Intelligence
Nasar, M., Kausar, M. A., & Musalhi, N. A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3311-3.ch007 · Cited by 5 · see citing works
Shahid, M. I., Badar, H. M. S., Asghar, M. N., & Abdullah, A. (2026) · Scientific Reports · doi:10.1038/s41598-026-36034-y · Cited by 5 · see citing works
A systematic review on privacy preservation in federated learning
Sum, A. S. I., Pritee, Z. T., Saha, A. K., Mahmud, M. A., & Nur, K. M. (2026) · International Journal of Information Security · doi:10.1007/s10207-026-01229-x · Cited by 6 · see citing works
Clinical AI in Radiology: Foundations, Trends, Applications, and Emerging Directions
Hartsock, I., Koutsoubis, N., Ahmed, S., Parker, N. H., Schabath, M. B., Araujo, C. R., Qayyum, A., Lam, C. A., Gatenby, R. A., & Rasool, G. (2026) · Cancers · doi:10.3390/cancers18060942 · Cited by 4 · see citing works
Privacy-Preserving Federated Deep-Equilibrium Learning for Medical Image Classification
Γκίλλας, Α., Ampeliotis, D., & Berberidis, K. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635684 · Cited by 4 · see citing works
Comparation of Federated and Centralized Learning for Image Classification
Mar’i, F., Supianto, A. A., & Bachtiar, F. A. (2023) · PIKSEL Penelitian Ilmu Komputer Sistem Embedded and Logic · doi:10.33558/piksel.v11i2.7367 · Cited by 4 · see citing works
Wal, P., Dwivedi, J., Pandey, K., Sharma, K. K., Tiwari, M., Ali, M. S., Khan, A., & Gasmi, A. (2026) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2026.109120 · Cited by 3 · see citing works
Rekha, M., & S.Senthilkumar (2026) · Iranian Journal of Science and Technology Transactions of Electrical Engineering · doi:10.1007/s40998-026-01120-4 · Cited by 3 · see citing works
Technical considerations of federated learning in digital healthcare systems
Alozie, E., Olagunju, H. I., Faruk, N., & Garba, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13897-3.00009-6 · Cited by 3 · see citing works
Specialty Society Support for Multicenter Research in Artificial Intelligence
Allen, B., Schmidt, K., Brink, L., Pisano, E. D., Coombs, L. P., Apgar, C., Dreyer, K. J., & Wald, C. (2023) · Academic Radiology · doi:10.1016/j.acra.2023.01.010 · Cited by 3 · see citing works
Liang, W., Zhang, W., Yan, H., Zong, Y., & Yan, C. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109388 · Cited by 2 · see citing works
Deep Learning for Digital Pathology in Limited Data Scenarios
Stacke, K. (2022) · Linköping studies in science and technology. Dissertations · doi:10.3384/9789179294748 · Cited by 2 · see citing works
Trivizakis, E., & Kostas Marias, A. (2023) · Imaging informatics for healthcare professionals · doi:10.1007/978-3-031-25928-9_6 · Cited by 2 · see citing works
Önsü, M. A., Kantarcı, B., & Boukerche, A. (2023) · International Conference on Communications · doi:10.1109/icc45041.2023.10279500 · Cited by 2 · see citing works
An Innovative Machine Learning Approach in Diagnostic Imaging Using Deep Learning
Sharma, D., Raghavendra, R., & Yadav, D. (2024) · 2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC) · doi:10.1109/icocwc60930.2024.10470639 · Cited by 1 · see citing works
RetractedHarnessing artificial intelligence for transpathology advancements
Liu, Z., Dong, S., Zhang, L., & Shi, K. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95223-1.00022-9 · Cited by 2 · see citing works
A Hybrid Deep Learning Approach for Brain Tumor Classification
Cruz, M. J. F. S., & Caro, J. D. L. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-73344-4_57 · Cited by 2 · see citing works
Fiszer, J., Ciupek, D., Malawski, M., & Pieciak, T. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.09.637305 · Cited by 2 · see citing works
A Federated Learning Approach to Bone Metastasis Prediction Using Convolutional Neural Network
Ali, S. M., Eunus, S. I., Bushra, T. A., Mehedi, M. H. K., & Rasel, A. A. (2023) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit60459.2023.10441154 · Cited by 1 · see citing works
Federated Learning Enable Training of Perception Model for Autonomous Driving
Liu, J., Yin, Z., Nie, L., & Zhao, X. (2024) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2024-01-2873 · Cited by 1 · see citing works
Edge-Based Detection of Label Flipping Attacks in Federated Learning Using Explainable AI
Alotaibi, N., Felemban, M., & Mahmood, S. (2025) · doi:10.1109/svm66695.2025.00005 · Cited by 1 · see citing works
Shi, Z., Gong, D., & Yan, X. (2025) · Lecture notes in operations research · doi:10.1007/978-981-96-9697-0_77 · Cited by 1 · see citing works
Kuznetsov, O., Melchiori, M., Galdelli, A., Frontoni, E., & Arnesano, M. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01014-8 · Cited by 1 · see citing works
A review of security threats and privacy issues in federated learning
Agrahari, A. K., Dinker, A. G., & Singh, R. B. (2026) · International Journal of Data Science and Analytics · doi:10.1007/s41060-026-01067-z · Cited by 3 · see citing works
Mirzaei, A., Nouri-Moghaddam, B., & Abdollahi, J. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01524-5 · Cited by 1 · see citing works
Zheng, P., Zhou, Y., Yu, X., Wang, W., & Liao, S. (2026) · Bulletin of the National Research Centre/Bulletin of the National Research Center · doi:10.1186/s42269-026-01452-5 · Cited by 1 · see citing works
Lin, B., Wang, J., Dou, Y., Zhang, Y., Yue, W., Yu, G., & Yin, J. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385560 · Cited by 1 · see citing works
Decentralized Diagnostics: The Role of Federated Learning in Modern Medical Imaging
Rahmaniar, W., Deng, Z., Yang, Y., Jin, Z., & Suzuki, K. (2024) · Intelligent systems reference library · doi:10.1007/978-3-031-65640-8_9 · Cited by 1 · see citing works
Presentation Attack Detection for Multispectral Face Biometric System Using Federated Learning
Purushothaman, M. M., Adapa, S. R., & Bellamkonda, S. (2024) · Information systems engineering and management · doi:10.1007/978-3-031-69201-7_22 · Cited by 1 · see citing works
Halabi, S. S. (2022) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2022.06.003 · Cited by 1 · see citing works
Bone Metastasis Prediction Using Federated LearningPreprint
Eunus, S. I. (2023) · doi:10.31219/osf.io/jez92
Privacy-Preserving Federated Learning for Brain MRI Analysis
Dcunha, L., Dsouza, L., Ramanan, G., & Chaudhari, S. (2023) · International Conference on Recent Trends in Advance Computing (ICRTAC) · doi:10.1109/icrtac59277.2023.10480812
Farzana, W., Temtam, A., & Iftekharuddin, K. M. (2024) · SPIE Medical Imaging: Clinical and Biomedical Imaging · doi:10.1117/12.3009710
Farzana, W., Witherow, M. A., Longoria, I., Sadique, M. S., Temtam, A., & Iftekharuddin, K. M. (2024) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.3008748
A Machine Learning Approach for Medical Imaging Evaluation
Qiu, W., Shi, D., Li, Q., Zheng, Y., & Shen, X. (2024) · International Conference on Bioinformatics and Intelligent Computing (BIC) · doi:10.1145/3665689.3665699
Better AI for Kids: Learning from the AI-OPiNE Study
Rafful, P. P., & Teixeira, S. R. (2024) · Radiology Artificial Intelligence · doi:10.1148/ryai.240376
Bridging the Gaps: Imputation of Parkinson’s Disease Clinical Assessments With Federated Learning
Reyes, J., Noroozi, A., Xiao, Y., & Kersten‐Oertel, M. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3593459 · Cited by 2 · see citing works
Liu, Y., Huang, J., Chen, J.-C., Chen, W., Pan, Y., & Qiu, J. (2024) · Figshare · doi:10.6084/m9.figshare.25980485
Liu, Y., Huang, J., Chen, J.-C., Chen, W., Pan, Y., & Qiu, J. (2024) · Figshare · doi:10.6084/m9.figshare.26730148
Liu, Y., Huang, J., Chen, J.-C., Chen, W., Pan, Y., & Qiu, J. (2024) · Figshare · doi:10.6084/m9.figshare.25980636
Fine-Tuning Hybrid CNN + LSTM for Brain Tumor Multiclass Classification
Cruz, M. J. F. S., Caro, J. D. L., & Solano, G. A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-10827-2_13
Application of artificial intelligence in gynecologic cancers: A bibliometric analysis
Liu, N., Jiang, F., Lou, Y., & Guan, J. (2026) · Digital Health · doi:10.1177/20552076261416708
Wang, J., Wang, T., Luo, S., Guo, X., Yin, J., Li, J., Yuan, Z., Yu, G., & Lin, B. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11357042
Fiszer, J., Ciupek, D., Malawski, M., & Pieciak, T. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109649 · Cited by 2 · see citing works
Gomathi, R., Saranya, K., John, Y. M. M., & Mary, G. L. R. (2026) · Scientific Reports · doi:10.1038/s41598-026-41469-4
Federated learning for privacy-preserving AI in healthcare applications
Nagvekar, M., Borkar, P. S., Badhiye, S. S., Janardhan, D. M., Sa, J., & Yenkikar, A. (2026) · AIP conference proceedings · doi:10.1063/5.0326181
M.Monisha, S.Thangamayan, & Lavaraju, B. (2025) · International Conference on Advancement in Futuristic Technologies (ICAFT) · doi:10.1109/icaft66710.2025.11453122
Don, A. K. K., & Khalil, I. (2026) · Quantum Machine Intelligence · doi:10.1007/s42484-026-00385-6 · Cited by 1 · see citing works
FedCycle: An Improved Federated Learning Framework for Assessment Across Modalities and Domains
Dündar, B., Sezer, E. A., Okay, F. Y., & Özdemi̇r, S. (2026) · Electronics · doi:10.3390/electronics15081752
Federated Learning for Privacy-Preserving Medical Prediction with Quality-Aware Aggregation
Anwar, M. S., Khan, A. N., & Raihan, M. (2025) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit68739.2025.11491342
Bundele, A. D., Deshpande, A. R., Jadhav, S. W., & Handge, Y. A. (2026) · International Conference on Computing, Communication and Green Engineering (CCGE) · doi:10.1109/ccge67142.2026.11581726
Trust Aware Federated Learning for Secure Bone Healing Stage Interpretation in e-Health
Shepherd, P., Dagiuklas, T., Alkan, B., Bastos, J., & Rodriguez, J. (2026) · International Conference on Communications · doi:10.1109/icc59461.2026.11587338
Gowtham, M., & P, L. M. (2026) · International Conference on Emerging Research in Smart Electronics and Machine Informatics (ECMI) · doi:10.1109/ecmi68341.2026.11603213
Periyasamy, T. S., Sekar, N., & Lakshmanan, H. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877261471812
Sowan, B. I., & Massad, B. (2026) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2026.101794
- Authors
- Darzidehkalani, E., Ghasemi-rad, M., & van Ooijen, P. M. A.
- Affiliations
- University Medical Center Groningen (NL)
- Baylor College of Medicine (US)
- Corresponding author
- Darzidehkalani, Erfan
- Venue
- Journal of the American College of Radiology
- Publisher
- Elsevier BV
- Published
- 2022-08
- Volume
- 19
- Issue
- 8
- Pages
- 975-982
- ISSN
- 1546-1440 (ISSN-L)
- OpenAlex
- W4224879845
- Cited by
- 103
- References
- 31
- Citations by year
- 2026: 25 · 2025: 22 · 2024: 35 · 2023: 17 · 2022: 4
- FWCI
- 6.42
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of the American College of Radiology (publishedVersion, cc-by)
- PubMed (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
- Funders
- Health~Holland
- NWO
- KWF Kankerbestrijding
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- federated learning, diagnostic imaging, data privacy, decentralized learning, deep neural network, medical data privacy
- MeSH terms
- Machine Learning, Algorithms, Humans, Neural Networks, Computer, Privacy
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
