Federated Learning for Breast Density Classification: A Real-World Implementation

The Measurement of Observer Agreement for Categorical Data

Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148 · see citing works

Densely Connected Convolutional Networks

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566 · see citing works

Mammographic Density and the Risk and Detection of Breast Cancer

Boyd, N. F., Guo, H., Martin, L. J., Sun, L., Stone, J., Fishell, E., Jong, R. A., Hislop, G., Chiarelli, A. M., Minkin, S., et al. (2007) · New England Journal of Medicine · doi:10.1056/nejmoa062790 · Cited by 2,482 · see citing works

Diagnostic Performance of Digital versus Film Mammography for Breast-Cancer Screening

Pisano, E. D., Gatsonis, C. A., Hendrick, E. R., Yaffe, M. J., Baum, J. K., Acharyya, S., Conant, E. F., Fajardo, L. L., Bassett, L. W., D'Orsi, C. J., et al. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa052911 · Cited by 1,937 · see citing works

Secure, privacy-preserving and federated machine learning in medical imaging

Kaissis, G. A., Makowski, M. R., Rückert, D., & Braren, R. F. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0186-1 · Cited by 1,501 · see citing works

Quantitative Classification of Mammographic Densities and Breast Cancer Risk: Results From the Canadian National Breast Screening Study

Boyd, N. F., Byng, J. W., Jong, R. A., Fishell, E., Little, L., Miller, A. B., Lockwood, G. A., Tritchler, D., & Yaffe, M. J. (1995) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/87.9.670 · Cited by 1,100 · see citing works

Multi-institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation

Sheller, M. J., Reina, G. A., Edwards, B., Martin, J., & Bakas, S. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-11723-8_9 · Cited by 609 · 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

Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results

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

Prevalence of Mammographically Dense Breasts in the United States

Sprague, B. L., Gangnon, R. E., Burt, V., Trentham‐Dietz, A., Hampton, J. M., Wellman, R., Kerlikowske, K. M., & Miglioretti, D. L. (2014) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/dju255 · 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

Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation

Lehman, C. D., Yala, A., Schuster, T., Dontchos, B., Bahl, M., Swanson, K., & Barzilay, R. (2019) · Radiology · doi:10.1148/radiol.2018180694 · Cited by 294 · see citing works

Mammographic Breast Density and Subsequent Risk of Breast Cancer in Postmenopausal Women According to Tumor Characteristics

Yaghjyan, L., Colditz, G. A., Collins, L., Schnitt, S. J., ROSNER, B. A., Vachon, C., & Tamimi, R. M. (2011) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djr225 · Cited by 230 · see citing works

Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs

Dunnmon, J. A., Yi, D., Langlotz, C. P., Ré, C., Rubin, D. L., & Lungren, M. P. (2019) · Radiology · doi:10.1148/radiol.2018181422 · Cited by 206 · see citing works

Variation in Mammographic Breast Density Assessments Among Radiologists in Clinical Practice

Sprague, B. L., Conant, E. F., Onega, T., Garcia, M. P., Beaber, E. F., Herschorn, S. D., Lehman, C. D., Tosteson, A. N. A., Lacson, R. C., Schnall, M. D., et al. (2016) · Annals of Internal Medicine · doi:10.7326/m15-2934 · Cited by 205 · see citing works

Ethics of using and sharing clinical imaging data for artificial intelligence: a proposed framework

Larson, D. B., Magnus, D. C., Lungren, M. P., Shah, N. H., & Langlotz, C. P. (2020) · Radiology · doi:10.1148/radiol.2020192536 · Cited by 199 · see citing works

A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols

Karani, N., Chaitanya, K., Baumgartner, C. F., & Ender Konukoglu, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00928-1_54 · Cited by 129 · see citing works

Mammography: Interobserver variability in breast density assessment

Ooms, E. A., Zonderland, H. M., Eijkemans, M. J. C., Kriege, M., Delavary, B. M., Burger, C. W., & Ansink, A. C. (2007) · The Breast · doi:10.1016/j.breast.2007.04.007 · Cited by 125 · see citing works

Reproducibility of BI-RADS Breast Density Measures Among Community Radiologists: A Prospective Cohort Study

Spayne, M. C., Gard, C. C., Skelly, J. M., Miglioretti, D. L., Vacek, P. M., & Geller, B. M. (2012) · The Breast Journal · doi:10.1111/j.1524-4741.2012.01250.x · Cited by 83 · see citing works

Mammographic density and breast cancer risk in White and African American Women

Razzaghi, H., Troester, M. A., Gierach, G. L., Olshan, A. F., Yankaskas, B. C., & Millikan, R. C. (2012) · Breast Cancer Research and Treatment · doi:10.1007/s10549-012-2185-3 · Cited by 80 · see citing works

Multi-Institutional Assessment and Crowdsourcing Evaluation of Deep Learning for Automated Classification of Breast Density

Chang, K., Beers, A. L., Brink, L., Patel, J., Singh, P., Arun, N., Hoebel, K., Gaw, N. B., Shah, M., Pisano, E. D., et al. (2020) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2020.05.015 · Cited by 51 · see citing works

Dense Breasts: A Review of Reporting Legislation and Available Supplemental Screening Options

Ho, J. M., Jafferjee, N., Covarrubias, G. M., Ghesani, M. V., & Handler, B. (2014) · American Journal of Roentgenology · doi:10.2214/ajr.13.11969 · Cited by 45 · see citing works

Towards continual learning in medical imagingPreprint

Baweja, C., Glocker, B., & Kamnitsas, K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.02496 · Cited by 45 · see citing works

Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE ResultsPreprint

Li, X., Gu, Y., Dvornek, N. C., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.05647 · Cited by 27 · see citing works

Federated learning for predicting clinical outcomes in patients with COVID-19

Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Wood, B. J., Tsai, C.-S., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01506-3 · Cited by 842 · see citing works

Federated learning for smart healthcare: A survey

Nguyen, D. C., Pham, Q.-V., Pathirana, P. N., Ding, M., Seneviratne, A., Lin, Z., Dobre, O., & Hwang, W.-J. (2023) · ACM Computing Surveys · doi:10.1145/3501296 · Cited by 827 · see citing works

FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space

Liu, Q., Chen, C., Qin, J., Dou, Q., & Heng, P. (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/cvpr46437.2021.00107 · Cited by 586 · see citing works

Privacy-preserving artificial intelligence in healthcare: Techniques and applications

Khalid, N., Qayyum, A., Bilal, M., Al-Fuqaha, A., & Qadir, J. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106848 · Cited by 581 · 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

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

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

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

Privacy preservation for federated learning in health care

Pati, S., Kumar, S., Varma, A., Edwards, B., Lu, C., Qu, L., Wang, J. J., Lakshminarayanan, A., Wang, S.-H., Sheller, M., et al. (2024) · Patterns · doi:10.1016/j.patter.2024.100974 · Cited by 269 · see citing works

Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review

Rani, S., Kataria, A., Kumar, S., & Tiwari, P. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110658 · Cited by 228 · see citing works

Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning

Qu, L., Zhou, Y., Liang, P. P., Xia, Y., Wang, F., Adeli, E., Fei-Fei, L., & Rubin, D. L. (2022) · 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/cvpr52688.2022.00982 · Cited by 220 · see citing works

Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges

Joshi, M., Pal, A., & Sankarasubbu, M. (2022) · ACM Transactions on Computing for Healthcare · doi:10.1145/3533708 · Cited by 218 · see citing works

HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images

Jiang, M., Wang, Z.-R., & Dou, Q. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19993 · Cited by 174 · see citing works

Federated Learning for Edge Computing: A Survey

Brecko, A., Kajáti, E., Koziorek, J., & Zolotová, I. (2022) · Applied Sciences · doi:10.3390/app12189124 · Cited by 162 · see citing works

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Luo, L., Wang, X., Lin, Y., Ma, X., Tan, A., Chan, R., Vardhanabhuti, V., Chu, C. W., Cheng, K.-T. T., & Chen, H. (2024) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2024.3357877 · Cited by 174 · see citing works

A Transfer Learning Approach to Breast Cancer Classification in a Federated Learning Framework

Tan, Y. N., Tinh, V. P., Lam, P. D., Nam, N. H., & Khoa, T. A. (2023) · IEEE Access · doi:10.1109/access.2023.3257562 · Cited by 149 · see citing works

Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment.

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

Federated Learning for Medical Image Analysis with Deep Neural Networks

Nazir, S., & Kaleem, M. (2023) · Diagnostics · doi:10.3390/diagnostics13091532 · Cited by 156 · see citing works

Specificity-Preserving Federated Learning for MR Image Reconstruction

Feng, C.-M., Yan, Y., Wang, S., Xu, Y. D., Shao, L., & Fu, H. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3202106 · Cited by 131 · see citing works

Federated learning for multi-center imaging diagnostics: a simulation study in cardiovascular disease

Linardos, A., Kushibar, K., Walsh, S., Gkontra, P., & Lekadir, K. (2022) · Scientific Reports · doi:10.1038/s41598-022-07186-4 · Cited by 130 · see citing works

Do Gradient Inversion Attacks Make Federated Learning Unsafe?

Hatamizadeh, A., Yin, H., Molchanov, P. A., Myronenko, A., Li, W., Dogra, P., Feng, A., Flores, M. G., Kautz, J., Xu, D., et al. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3239391 · Cited by 133 · see citing works

Federated Learning of Generative Image Priors for MRI Reconstruction

Elmas, G., Dar, S. U. H., Korkmaz, Y., Ceyani, E., Susam, B., Özbey, M., Avestimehr, S., & Çukur, T. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3220757 · Cited by 117 · see citing works

A systematic review on federated learning in medical image analysis

Sohan, M. F., & Basalamah, A. (2023) · IEEE Access · doi:10.1109/access.2023.3260027 · Cited by 114 · 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

Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching

Liu, Q., Yang, H., Dou, Q., & Heng, P. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87199-4_31 · Cited by 109 · see citing works

Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine

Chaddad, A., Lu, Q., Li, J., Katib, Y., Kateb, R., Tanougast, C., Bouridane, A., & Abdulkadir, A. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123123 · Cited by 112 · see citing works

A comprehensive review on federated learning based models for healthcare applications

Sharma, S., & Guleria, K. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102691 · Cited by 107 · see citing works

Personalized Retrogress-Resilient Federated Learning Toward Imbalanced Medical Data

Chen, Z., Yang, C., Zhu, M., Peng, Z., & Yuan, Y. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3192483 · Cited by 98 · see citing works

Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review

Gastounioti, A., Desai, S., Ahluwalia, V. S., Conant, E. F., & Despina Kontos, B. (2022) · Breast Cancer Research · doi:10.1186/s13058-022-01509-z · Cited by 96 · see citing works

IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation

Jiang, M., Yang, H., Chen, C., & Dou, Q. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3263072 · Cited by 77 · see citing works

Secure neuroimaging analysis using federated learning with homomorphic encryption

Stripelis, D., Saleem, H., Ghai, T., Dhinagar, N. J., Gupta, U., Anastasiou, C., Steeg, G. V., Ravi, S., Naveed, M., Thompson, P. M., et al. (2021) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1117/12.2606256 · Cited by 72 · see citing works

A Review of Medical Federated Learning: Applications in Oncology and Cancer Research

Chowdhury, A., Kassem, H., Padoy, N., Umeton, R., & Karargyris, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08999-2_1 · Cited by 70 · see citing works

Generalizability challenges of mortality risk prediction models: A retrospective analysis on a multi-center database

Singh, H., Mhasawade, V., & Chunara, R. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000023 · Cited by 67 · see citing works

Federated Semi-Supervised Medical Image Segmentation via Prototype-Based Pseudo-Labeling and Contrastive Learning

Wu, H., Zhang, B., Chen, C., & Qin, J. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3314430 · Cited by 65 · see citing works

Federated Learning in Dentistry: Chances and Challenges

Rischke, R., Schneider, L., Müller, K. R., Samek, W., Schwendicke, F., & Krois, J. (2022) · Journal of Dental Research · doi:10.1177/00220345221108953 · Cited by 67 · see citing works

Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation

Guo, P., Yang, D. L., Hatamizadeh, A., Xu, A. P., Xu, Z., Li, W., Zhao, C. H., Xu, D., Harmon, S. A., Turkbey, E. B., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19803-8_26 · Cited by 56 · see citing works

Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning

Shiri, I., Sadr, A. V., Akhavan, A., Salimi, Y., Sanaat, A., Amini, M., Razeghi, B., Saberi, A., Arabi, H., Ferdowsi, S., et al. (2022) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-022-06053-8 · Cited by 57 · see citing works

Decentralized Distributed Multi-institutional PET Image Segmentation Using a Federated Deep Learning Framework

Shiri, I., Sadr, A. V., Amini, M., Salimi, Y., Sanaat, A., Akhavanallaf, A., Razeghi, B., Ferdowsi, S., Saberi, A., Arabi, H., et al. (2022) · Clinical Nuclear Medicine · doi:10.1097/rlu.0000000000004194 · Cited by 53 · see citing works

The Federated Tumor Segmentation (FeTS) ChallengePreprint

Pati, S., Baid, U., Zenk, M., Edwards, B., Sheller, M., Reina, G. A., Foley, P., Gruzdev, A., Martin, J., Albarqouni, S., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.05874 · Cited by 102 · see citing works

The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research

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

Reliable customer analysis using federated learning and exploring deep-attention edge intelligence

Ahmed, U., Srivastava, G., & Lin, J. C.-W. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2021.08.028 · Cited by 52 · see citing works

Decentralized Learning in Healthcare: A Review of Emerging Techniques

Shiranthika, C., Saeedi, P., & Bajić, I. V. (2023) · IEEE Access · doi:10.1109/access.2023.3281832 · Cited by 52 · see citing works

Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning

Chen, Z., Zhu, M., Yang, C., & Yuan, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87199-4_33 · Cited by 47 · see citing works

Federated Learning For IoT: Applications, Trends, Taxonomy, Challenges, Current Solutions, and Future Directions

Adam, M., & Baroudi, U. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3506214 · Cited by 46 · see citing works

FedPerl: Semi-supervised Peer Learning for Skin Lesion Classification

Bdair, T. M., Navab, N., & Albarqouni, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87199-4_32 · Cited by 46 · see citing works

Privacy-Preserving Generative Adversarial Network for Case-Based Explainability in Medical Image Analysis

Montenegro, H., Silva, W., & Cardoso, J. S. (2021) · IEEE Access · doi:10.1109/access.2021.3124844 · Cited by 46 · see citing works

Privacy-preserving blockchain-based federated learning for brain tumor segmentation

Kumar, R., Bernard, C. M., Ullah, A., Khan, R. U., Kumar, J., Kulevome, D. K. B., Rao, Y., & Zeng, S. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108646 · Cited by 45 · see citing works

Artificial intelligence for breast cancer detection and its health technology assessment: A scoping review

Uwimana, A., Gnecco, G., & Riccaboni, M. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109391 · Cited by 46 · see citing works

A Collaborative Federated Learning Framework for Lung and Colon Cancer Classifications

Hossain, M. M., Islam, M. R., Ahamed, M. F., Ahsan, M., & Haider, J. (2024) · Technologies · doi:10.3390/technologies12090151 · Cited by 44 · see citing works

FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information

Ho, T.-T., Tran, K.-D., & Huang, Y. (2022) · Sensors · doi:10.3390/s22103728 · Cited by 44 · see citing works

Federated Learning for Privacy-Preserved Medical Internet of Things

Thilakarathne, N. N., Muneeswari, G., Parthasarathy, V., Alassery, F., Hamam, H., Mahendran, R. K., & Shafiq, M. (2022) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2022.023763 · Cited by 46 · see citing works

Collaborative Privacy-preserving Approaches for Distributed Deep Learning Using Multi-Institutional Data

Gupta, S., Kumar, S., Chang, K., Lu, C., Singh, P., & Kalpathy–Cramer, J. (2023) · Radiographics · doi:10.1148/rg.220107 · Cited by 44 · see citing works

A Federated Learning Approach to Breast Cancer Prediction in a Collaborative Learning Framework

Almufareh, M. F., Tariq, N., Humayun, M., & Almas, B. (2023) · Healthcare · doi:10.3390/healthcare11243185 · Cited by 43 · see citing works

Privacy-Preserving Federated Learning in Healthcare

Moon, S., & Lee, W. H. (2023) · International Conference on Electronics, Information, and Communication (ICEIC) · doi:10.1109/iceic57457.2023.10049966 · Cited by 42 · see citing works

Federated Learning in Computer Vision

Shenaj, D., Rizzoli, G., & Zanuttigh, P. (2023) · IEEE Access · doi:10.1109/access.2023.3310400 · Cited by 45 · 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

Fedsld: Federated Learning with Shared Label Distribution for Medical Image Classification

Luo, J., & Wu, S. (2022) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi52829.2022.9761404 · Cited by 40 · see citing works

Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image SegmentationPreprint

Xia, Y., Yang, D., Li, W., Myronenko, A., Xu, D., Obinata, H., Mori, H., An, P., Harmon, S. A., Turkbey, E. B., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.10195 · Cited by 39 · see citing works

Federated Multi-Organ Segmentation With Inconsistent Labels

Xu, X., Deng, H., Gateño, J., & Yan, P. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3270140 · Cited by 37 · see citing works

Transformer versus traditional natural language processing: how much data is enough for automated radiology report classification?

Yang, E., Li, M., Raghavan, S., Deng, F. M., Lang, M., Succi, M. D., Huang, A. J., & Kalpathy–Cramer, J. (2023) · British Journal of Radiology · doi:10.1259/bjr.20220769 · Cited by 36 · see citing works

CrowdGuard: Federated Backdoor Detection in Federated Learning

Rieger, P., Krauß, T., Miettinen, M., Dmitrienko, A., & Sadeghi, A.-R. (2024) · Proceedings 2024 Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.23233 · Cited by 36 · see citing works

MESAS: Poisoning Defense for Federated Learning Resilient against Adaptive Attackers

Krauß, T., & Dmitrienko, A. (2023) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3576915.3623212 · Cited by 35 · see citing works

Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare

Gawali, M., Arvind, C. S., Suryavanshi, S., Madaan, H., Gaikwad, A., Prakash, B., Kulkarni, V., & Pant, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-80432-9_34 · Cited by 35 · see citing works

Handling data heterogeneity with generative replay in collaborative learning for medical imaging

Qu, L., Balachandar, N., Zhang, M., & Rubin, D. L. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102424 · Cited by 35 · see citing works

FedBrain: A robust multi-site brain network analysis framework based on federated learning for brain disease diagnosis

Zhang, C., Meng, X., Liu, Q., Wu, S., Wang, L., & Ning, H. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126791 · Cited by 32 · see citing works

Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance

Jiang, M., Yang, H., Li, X., Liu, Q., Heng, P., & Dou, Q. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16437-8_19 · Cited by 31 · see citing works

E2EGI: End-to-End Gradient Inversion in Federated Learning

Li, Z., Wang, L., Chen, G., Zhang, Z., Shafiq, M., & Gu, Z. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3204455 · Cited by 30 · see citing works

CPS Attack Detection under Limited Local Information in Cyber Security: An Ensemble Multi-Node Multi-Class Classification Approach

Liu, J., Tang, Y., Zhao, H., Wang, X., Li, F., & Zhang, J. (2023) · ACM Transactions on Sensor Networks · doi:10.1145/3585520 · Cited by 29 · see citing works

Federated adaptive reweighting for medical image classification

Ma, B., Feng, Y., Chen, G., Li, C., & Xia, Y. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109880 · Cited by 29 · see citing works

LEAF: A Federated Learning-Aware Privacy-Preserving Framework for Healthcare Ecosystem

Patel, N., Parekh, R., Amin, S. A., Gupta, R., Tanwar, S., Kumar, N., Iqbal, R., & Sharma, R. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3287393 · Cited by 28 · see citing works

Medical federated learning with joint graph purification for noisy label learning

Chen, Z., Li, W., Xing, X., & Yuan, Y. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102976 · Cited by 28 · see citing works

Tackling heterogeneity in medical federated learning via aligning vision transformers

Darzidehkalani, E., Shen, Y., Ou, Y., Sijtsema, N. M., & van Ooijen, P. M. A. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102936 · Cited by 27 · see citing works

Sensitivity-Aware Differential Privacy for Federated Medical Imaging

Zheng, L., Cao, Y., Yoshikawa, M., Shen, Y., Rashed, E. A., Taura, K., Hanaoka, S., & Zhang, T. (2025) · Sensors · doi:10.3390/s25092847 · Cited by 27 · see citing works

Automatic normalized digital color staining in the recognition of abnormal blood cells using generative adversarial networks

Barrera, K., Rodellar, J., Alférez, S., & Merino, A. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107629 · Cited by 26 · see citing works

Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data

Ziegler, J., Pfitzner, B., Schulz, H., Saalbach, A., & Bert Arnrich, A. (2022) · Sensors · doi:10.3390/s22145195 · Cited by 27 · see citing works

Understanding Partnership Formation and Repeated Contributions in Federated Learning: An Analytical Investigation

Bi, X., Gupta, A., & Yang, M. (2023) · Management Science · doi:10.1287/mnsc.2023.00611 · Cited by 25 · see citing works

Federated Learning: Issues in Medical Application

Yoo, J. H., Jeong, H., Lee, J., & Chung, T. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-91387-8_1 · Cited by 26 · see citing works

Federated Deep Learning for Wireless Capsule Endoscopy Analysis: Enabling Collaboration Across Multiple Data Centers for Robust Learning of Diverse Pathologies

Wahab, H., Mehmood, I., Ugail, H., Ser, J. D., & Muhammad, K. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.10.007 · Cited by 23 · see citing works

Lightweight and Dynamic Privacy-Preserving Federated Learning via Functional Encryption

Yu, B., Zhao, J., Zhang, K., Gong, J., & Qian, H. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3540312 · Cited by 25 · see citing works

Enhancing Alzheimer’s disease classification through split federated learning and GANs for imbalanced datasets

Nimeshika, G. N., & Subitha, D. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2459 · Cited by 22 · see citing works

GRACE: A Generalized and Personalized Federated Learning Method for Medical Imaging

Zhang, R., Fan, Z., Xu, Q., Yao, J., Zhang, Y., & Wang, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43898-1_2 · Cited by 22 · see citing works

Decentralised Federated Learning for Hospital Networks With Application to COVID-19 Detection

Giuseppi, A., Manfredi, S., Menegatti, D., Poli, C., & Pietrabissa, A. (2022) · IEEE Access · doi:10.1109/access.2022.3202922 · Cited by 22 · see citing works

Methods and Impact for Using Federated Learning to Collaborate on Clinical Research

Cheung, A. T. M., Nasir-Moin, M., Kwon, Y. J., Guan, J., Liu, C., Jiang, L. Y., Raimondo, C. D., Chotai, S., Chambless, L. B., Ahmad, H. S., et al. (2022) · Neurosurgery · doi:10.1227/neu.0000000000002198 · Cited by 21 · see citing works

Bilevel Hyperparameter Optimization and Neural Architecture Search for Enhanced Breast Cancer Detection in Smart Hospitals Interconnected With Decentralized Federated Learning Environment

Khan, S., Nosheen, F., Naqvi, S. S. A., Jamil, H., Faseeh, M., Khan, M. A., & Kim, D. (2024) · IEEE Access · doi:10.1109/access.2024.3392572 · Cited by 21 · see citing works

Federated Learning for Site Aware Chest Radiograph Screening

Chakravarty, A., Kar, A., Sethuraman, R., & Sheet, D. (2021) · 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9433876 · Cited by 24 · 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

Federation Opacity and the Promise of Federated Learning in Healthcare

Hatherley, J. J., Søgaard, A., Ballantyne, A., & Pauwels, R. (2026) · The American Journal of Bioethics · doi:10.1080/15265161.2026.2637093 · Cited by 25 · see citing works

A Federated Learning Approach to Tumor Detection in Colon Histology Images

Gunesli, G. N., Bilal, M., Raza, S. E. A., & Rajpoot, N. (2023) · Journal of Medical Systems · doi:10.1007/s10916-023-01994-5 · Cited by 20 · see citing works

IHVFL: a privacy-enhanced intention-hiding vertical federated learning framework for medical data

Tang, F., Liang, S., Ling, G., & Shan, J. (2023) · Cybersecurity · doi:10.1186/s42400-023-00166-9 · Cited by 20 · see citing works

Dense Contrastive-Based Federated Learning for Dense Prediction Tasks on Medical Images

Yang, Y., Liu, X., Gao, T., Xu, X., Zhang, P., & Wang, G. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3357947 · Cited by 19 · see citing works

Federated Learning with Deep Neural Networks: A Privacy-Preserving Approach to Enhanced ECG Classification

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

Applying YOLOv6 as an ensemble federated learning framework to classify breast cancer pathology images

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

A Privacy-Preserving Collaborative Federated Learning Framework for Detecting Retinal Diseases

Gulati, S., Guleria, K., Goyal, N., AlZubi, A. A., & Castilla, Á. K. (2024) · IEEE Access · doi:10.1109/access.2024.3493946 · Cited by 17 · see citing works

Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge

Zenk, M., Baid, U., Pati, S., Linardos, A., Edwards, B., Sheller, M., Foley, P., Aristizábal, A., Zimmerer, D., Gruzdev, A. D., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60466-1 · Cited by 19 · see citing works

Domain generalization of deep networks for medical image segmentation via meta learning

Liu, Q., Dou, Q., Chen, C., & Heng, P. (2022) · Elsevier eBooks · doi:10.1016/b978-0-32-399851-2.00016-8 · Cited by 14 · see citing works

Class Imbalanced Medical Image Classification Based on Semi-Supervised Federated Learning

Liu, W., Mo, J., & Zhong, F. (2023) · Applied Sciences · doi:10.3390/app13042109 · Cited by 13 · see citing works

Federated Learning for Computer-Aided Diagnosis of Glaucoma Using Retinal Fundus Images

Baptista, T., Soares, C., Oliveira, T., & Soares, F. (2023) · Applied Sciences · doi:10.3390/app132111620 · Cited by 13 · see citing works

Federated Learning: A Cross‐Institutional Feasibility Study of Deep Learning Based Intracranial Tumor Delineation Framework for Stereotactic Radiosurgery

Lee, W., Hong, J., Lin, Y., Lu, Y., Hsu, Y., Lee, C., Yang, H., Wu, C., Lu, C., Sun, M., et al. (2023) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28950 · Cited by 13 · see citing works

FedGuard: Selective Parameter Aggregation for Poisoning Attack Mitigation in Federated Learning

Chelli, M., Prigent, C., Schubotz, R., Costan, A., Antoniu, G., Cudennec, L., & Slusallek, P. (2023) · IEEE International Conference on Cluster Computing (CLUSTER) · doi:10.1109/cluster52292.2023.00014 · Cited by 13 · see citing works

Transfer Learning in Breast Density Classification

Chowderpally, S. S., Sanagala, S. S., & Vedula, S. V. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-1267-3_24

Fair evaluation of federated learning algorithms for automated breast density classification: The results of the 2022 ACR-NCI-NVIDIA federated learning challenge

Schmidt, K., Bearce, B., Chang, K., Coombs, L. P., Farahani, K., Elbatel, M., Mouheb, K., Martí, R., Zhang, R., Zhang, Y., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103206 · Cited by 9 · see citing works

Towards generalizable Federated Learning in medical imaging: A real-world case study on mammography data

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

Evaluating federated learning approaches for mammography under breast density heterogeneity

Quintana, G. I., Maria, F. M. D., & Vancamberg, L. (2026) · Journal of medical imaging · doi:10.1117/1.jmi.13.4.044504

MammoFL: Mammographic Breast Density Estimation using Federated LearningPreprint

Muthukrishnan, R., Heyler, A., Katti, K., Pati, S., Mankowski, W. C., Alahari, A., Sanborn, M., Conant, E. F., Scott, C. G., Winham, S. J., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2206.05575