ArticleJMIR medical informatics2021
Federated Learning for Thyroid Ultrasound Image Analysis to Protect Personal Information: Validation Study in a Real Health Care Environment.
Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 71 citations in OpenAlex.
- From Bench-to-Bedside: How Artificial Intelligence is Changing Thyroid Nodule Diagnostics, a Systematic Review.The Journal of clinical endocrinology and metabolism · 2024Pooled it
- Artificial intelligence in thyroid nodules and cancer: clinical validation and real-world performance.Endocrine connections · 2026Article
- Comparing decentralized machine learning and AI clinical models to local and centralized alternatives: a systematic review.NPJ digital medicine · 2026Article
- Privacy preserving skin cancer diagnosis through federated deep learning and explainable AI.Scientific reports · 2025Article
- The Potential of AI in Nursing Care: Multicenter Evaluation in Fall Risk Assessment.Journal of medical Internet research · 2025Article
- Air Pollutant Traceability Based on Federated Learning of Edge Intelligent Perception Agents.Sensors (Basel, Switzerland) · 2025Article
- Overfit detection method for deep neural networks trained to beamform ultrasound images.Ultrasonics · 2025Article
- New ways to use imaging data in cardiovascular research: survey of opinions on federated learning and synthetic data.European heart journal. Imaging methods and practice · 2025Article
- Explainable federated learning scheme for secure healthcare data sharing.Health information science and systems · 2024Article
- Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading.Computational and structural biotechnology journal · 2024Article
- Survey of Medical Applications of Federated Learning.Healthcare informatics research · 2024Article
- Medical Imaging Applications of Federated Learning.Diagnostics (Basel, Switzerland) · 2023Review
- Federated learning for medical imaging radiology.The British journal of radiology · 2023Review
- Federated Learning for Medical Image Analysis with Deep Neural Networks.Diagnostics (Basel, Switzerland) · 2023Review
- Reviewing Federated Machine Learning and Its Use in Diseases Prediction.Sensors (Basel, Switzerland) · 2023Review
- A Comprehensive Survey on Federated Learning Techniques for Healthcare Informatics.Computational intelligence and neuroscience · 2023Review
- Application of artificial intelligence in gynecologic cancers: A bibliometric analysis.Digital healthArticle
Corrections and comments
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Authors and funding
16 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFederated learning is a decentralized approach to machine learning; it is a training strategy that overcomes medical data privacy regulations and generalizes deep learning algorithms. Federated learning mitigates many systemic privacy risks by sharing only the model and parameters for training, without the need to export existing medical data sets. In this study, we performed ultrasound image analysis using federated learning to predict whether thyroid nodules were benign or malignant.
objectiveThe goal of this study was to evaluate whether the performance of federated learning was comparable with that of conventional deep learning.
methodsA total of 8457 (5375 malignant, 3082 benign) ultrasound images were collected from 6 institutions and used for federated learning and conventional deep learning. Five deep learning networks (VGG19, ResNet50, ResNext50, SE-ResNet50, and SE-ResNext50) were used. Using stratified random sampling, we selected 20% (1075 malignant, 616 benign) of the total images for internal validation. For external validation, we used 100 ultrasound images (50 malignant, 50 benign) from another institution.
resultsFor internal validation, the area under the receiver operating characteristic (AUROC) curve for federated learning was between 78.88% and 87.56%, and the AUROC for conventional deep learning was between 82.61% and 91.57%. For external validation, the AUROC for federated learning was between 75.20% and 86.72%, and the AUROC curve for conventional deep learning was between 73.04% and 91.04%.
conclusionsWe demonstrated that the performance of federated learning using decentralized data was comparable to that of conventional deep learning using pooled data. Federated learning might be potentially useful for analyzing medical images while protecting patients' personal information.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.