ReviewDiagnostics (Basel, Switzerland)2023
Federated Learning for Medical Image Analysis with Deep Neural Networks.
Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Digital Pathology Archives as Long-Term Clinical Memory: A Self-Sovereign Identity-Based Governance Model.Healthcare (Basel, Switzerland) · 2026Article
- FedSwin-LHTP: Structure-Aware Hessian-Inspired Token Pruning for Efficient Federated Skin Lesion Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- Swin UNETR-based prediction of [BMC medical imaging · 2026Article
- Leveraging large language models for gastrointestinal injury detection in athletes: a medical image analysis approach.Scientific reports · 2026Article
- Federated Privacy-Preserving Multi-Modal Deep Learning for Breast Cancer Diagnosis: A Physics-Aware Approach.Diagnostics (Basel, Switzerland) · 2026Article
- Adaptive homomorphic federated learning framework for multi-institutional medical imaging with optimized diagnostic accuracy.Scientific reports · 2026Article
- Federated Learning in Healthcare: From Research to Real-World Deployment.Annual review of biomedical engineering · 2026Review
- Stochastic Poisson-embedded privacy framework for federated learning with secure homomorphic encryption in medical AI.Scientific reports · 2026Article
- Federated learning for privacy-preserving skin cancer classification using deep neural networks.Frontiers in oncology · 2026Article
- Federated Learning for Histopathology Image Classification: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- AI-driven medical image analysis for sports injury diagnosis and prevention.Scientific reports · 2025Article
- Federated Learning in Public Health: A Systematic Review of Decentralized, Equitable, and Secure Disease Prevention Approaches.Healthcare (Basel, Switzerland) · 2025Review
- Hybrid pre trained model based feature extraction for enhanced indoor scene classification in federated learning environments.Scientific reports · 2025Article
- A federated learning-based privacy-preserving image processing framework for brain tumor detection from CT scans.Scientific reports · 2025Article
- Article
- Deep learning-driven approach for cataract management: towards precise identification and predictive analytics.Frontiers in cell and developmental biology · 2025Review
- PPFL: A personalized progressive federated learning method for leveraging different healthcare institution-specific features.iScience · 2024Article
- MyThisYourThat for interpretable identification of systematic bias in federated learning for biomedical images.NPJ digital medicine · 2024Article
- Optimizing Rare Disease Gait Classification through Data Balancing and Generative AI: Insights from Hereditary Cerebellar Ataxia.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical image analysis using deep neural networks (DNN) has demonstrated state-of-the-art performance in image classification and segmentation tasks, aiding disease diagnosis. The accuracy of the DNN is largely governed by the quality and quantity of the data used to train the model. However, for the medical images, the critical security and privacy concerns regarding sharing of local medical data across medical establishments precludes exploiting the full DNN potential for clinical diagnosis. The federated learning (FL) approach enables the use of local model's parameters to train a global model, while ensuring data privacy and security. In this paper, we review the federated learning applications in medical image analysis with DNNs, highlight the security concerns, cover some efforts to improve FL model performance, and describe the challenges and future research directions.
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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.