This paper is in the following e-collection/theme issue:

Machine Learning (1501) Artificial Intelligence (1421) Clinical Informatics (1050) Ethics, Privacy, and Legal Issues (494) Decision Support for Health Professionals (1210) Imaging Informatics (200) Clinical Information and Decision Making (1434) Secondary Use of Clinical Data for Research and Surveillance (375)

Published on in Vol 9, No 5 (2021): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25869, first published .
Federated Learning for Thyroid Ultrasound Image Analysis to Protect Personal Information: Validation Study in a Real Health Care Environment

Federated Learning for Thyroid Ultrasound Image Analysis to Protect Personal Information: Validation Study in a Real Health Care Environment

Federated Learning for Thyroid Ultrasound Image Analysis to Protect Personal Information: Validation Study in a Real Health Care Environment

Authors of this article:

Haeyun Lee1, 2 Author Orcid Image ;   Young Jun Chai3 Author Orcid Image ;   Hyunjin Joo1, 4 Author Orcid Image ;   Kyungsu Lee1, 2 Author Orcid Image ;   Jae Youn Hwang2 Author Orcid Image ;   Seok-Mo Kim5 Author Orcid Image ;   Kwangsoon Kim6 Author Orcid Image ;   Inn-Chul Nam7 Author Orcid Image ;   June Young Choi8 Author Orcid Image ;   Hyeong Won Yu8 Author Orcid Image ;   Myung-Chul Lee9 Author Orcid Image ;   Hiroo Masuoka10 Author Orcid Image ;   Akira Miyauchi10 Author Orcid Image ;   Kyu Eun Lee1, 11 Author Orcid Image ;   Sungwan Kim1, 4, 12 Author Orcid Image ;   Hyoun-Joong Kong1, 4, 13 Author Orcid Image
Article Authors Cited by (38) Tweetations (2) Metrics

Journals

  1. Lim J, Hong M, Lam W, Zhang Z, Teo Z, Liu Y, Ng W, Foo L, Ting D. Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology. Current Opinion in Ophthalmology 2022;33(3):174 View
  2. Launet L, Wang Y, Colomer A, Igual J, Pulgarín-Ospina C, Koulouzis S, Bianchi R, Mosquera-Zamudio A, Monteagudo C, Naranjo V, Zhao Z. Federating Medical Deep Learning Models from Private Jupyter Notebooks to Distributed Institutions. Applied Sciences 2023;13(2):919 View
  3. Prayitno , Shyu C, Putra K, Chen H, Tsai Y, Hossain K, Jiang W, Shae Z. A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications. Applied Sciences 2021;11(23):11191 View
  4. Antunes R, André da Costa C, Küderle A, Yari I, Eskofier B. Federated Learning for Healthcare: Systematic Review and Architecture Proposal. ACM Transactions on Intelligent Systems and Technology 2022;13(4):1 View
  5. Shiri I, Vafaei Sadr A, Amini M, Salimi Y, Sanaat A, Akhavanallaf A, Razeghi B, Ferdowsi S, Saberi A, Arabi H, Becker M, Voloshynovskiy S, Gündüz D, Rahmim A, Zaidi H. Decentralized Distributed Multi-institutional PET Image Segmentation Using a Federated Deep Learning Framework. Clinical Nuclear Medicine 2022;47(7):606 View
  6. Moshawrab M, Adda M, Bouzouane A, Ibrahim H, Raad A. Reviewing Federated Machine Learning and Its Use in Diseases Prediction. Sensors 2023;23(4):2112 View
  7. Dasaradharami Reddy K, Gadekallu T, Doulamis A. A Comprehensive Survey on Federated Learning Techniques for Healthcare Informatics. Computational Intelligence and Neuroscience 2023;2023(1) View
  8. Jithish J, Alangot B, Mahalingam N, Yeo K. Distributed Anomaly Detection in Smart Grids: A Federated Learning-Based Approach. IEEE Access 2023;11:7157 View
  9. Majeed A, Zhang X, Hwang S. Applications and Challenges of Federated Learning Paradigm in the Big Data Era with Special Emphasis on COVID-19. Big Data and Cognitive Computing 2022;6(4):127 View
  10. Joshi M, Pal A, Sankarasubbu M. Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges. ACM Transactions on Computing for Healthcare 2022;3(4):1 View
  11. Li H, Li C, Wang J, Yang A, Ma Z, Zhang Z, Hua D. Review on security of federated learning and its application in healthcare. Future Generation Computer Systems 2023;144:271 View
  12. Nazir S, Kaleem M. Federated Learning for Medical Image Analysis with Deep Neural Networks. Diagnostics 2023;13(9):1532 View
  13. Joseph J, Raveendranatha Panicker M, Tung Chen Y, Chandrasekharan K, Chacko Mondy V, Ayyappan A, Valakkada J, Vishnu Narayan K. lungEcho - Resource Constrained Lung Ultrasound Video Analysis Tool for Faster Triaging and Active Learning. Biomedical Engineering Advances 2023;6:100094 View
  14. Sandhu S, Gorji H, Tavakolian P, Tavakolian K, Akhbardeh A. Medical Imaging Applications of Federated Learning. Diagnostics 2023;13(19):3140 View
  15. Habchi Y, Himeur Y, Kheddar H, Boukabou A, Atalla S, Chouchane A, Ouamane A, Mansoor W. AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions. Systems 2023;11(10):519 View
  16. Rehman M, Hugo Lopez Pinaya W, Nachev P, Teo J, Ourselin S, Cardoso M. Federated learning for medical imaging radiology. The British Journal of Radiology 2023;96(1150) View
  17. Sharma S, Guleria K. A comprehensive review on federated learning based models for healthcare applications. Artificial Intelligence in Medicine 2023;146:102691 View
  18. Liu Y. Depression clinical detection model based on social media: a federated deep learning approach. The Journal of Supercomputing 2024;80(6):7931 View
  19. Kareem A, Liu H, Velisavljevic V. A federated learning framework for pneumonia image detection using distributed data. Healthcare Analytics 2023;4:100204 View
  20. Zukaib U, Cui X, Hassan M, Harris S, Hadi H, Zheng C. Blockchain and Machine Learning in EHR Security: A Systematic Review. IEEE Access 2023;11:130230 View
  21. Xu B, Zhang Y, Fan Z, Han L, Shen Z. Patient privacy protection: Generating available medical treatment plans based on federated learning and CBR. Information & Management 2024;61(7):103908 View
  22. Vo V, Shin T, Yang H, Kang S, Kim S. A comparison between centralized and asynchronous federated learning approaches for survival outcome prediction using clinical and PET data from non-small cell lung cancer patients. Computer Methods and Programs in Biomedicine 2024;248:108104 View
  23. Kong F, Wang X, Xiang J, Yang S, Wang X, Yue M, Zhang J, Zhao J, Han X, Dong Y, Zhu B, Wang F, Liu Y. Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading. Computational and Structural Biotechnology Journal 2024;23:1439 View
  24. Choi G, Cha W, Lee S, Shin S. Survey of Medical Applications of Federated Learning. Healthcare Informatics Research 2024;30(1):3 View
  25. Sant V, Radhachandran A, Ivezic V, Lee D, Livhits M, Wu J, Masamed R, Arnold C, Yeh M, Speier W. From Bench-to-Bedside: How Artificial Intelligence is Changing Thyroid Nodule Diagnostics, a Systematic Review. The Journal of Clinical Endocrinology & Metabolism 2024;109(7):1684 View
  26. Cao B, Zhao J, Liu X, Li Y. Adaptive 5G-and-beyond network-enabled interpretable federated learning enhanced by neuroevolution. Science China Information Sciences 2024;67(7) View
  27. Hernandez-Cruz N, Saha P, Sarker M, Noble J. Review of Federated Learning and Machine Learning-Based Methods for Medical Image Analysis. Big Data and Cognitive Computing 2024;8(9):99 View
  28. Zeydan E, Arslan S, Liyanage M. Managing Distributed Machine Learning Lifecycle for Healthcare Data in the Cloud. IEEE Access 2024;12:115750 View
  29. Zhao L, Xie H, Zhong L, Wang Y. Explainable federated learning scheme for secure healthcare data sharing. Health Information Science and Systems 2024;12(1) View
  30. Krishnamoorthy U, Balasubramani S. Intelligent Nanomaterial Image Characterizations – A Comprehensive Review on AI Techniques that Power the Present and Drive the Future of Nanoscience. Advanced Theory and Simulations 2024;7(12) View
  31. Pratihar R, Sankar R. Advancements in Parkinson’s Disease Diagnosis: A Comprehensive Survey on Biomarker Integration and Machine Learning. Computers 2024;13(11):293 View

Books/Policy Documents

  1. Chowdhury A, Kassem H, Padoy N, Umeton R, Karargyris A. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. View
  2. Obakhena H, Imoize A, Anyasi F. Federated Learning for Digital Healthcare Systems. View