ArticleProceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition2021
Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning.
Article in Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review and Identification of the Challenges of Deep Learning Techniques for Undersampled Magnetic Resonance Image Reconstruction.Sensors (Basel, Switzerland) · 2024Pooled it
- US-ATHC: Unsupervised Multi-Class Glioma Segmentation via Adaptive Thresholding and Clustering.Biomedicines · 2026Article
- Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration.ArXiv · 2025Article
- Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading.Computational and structural biotechnology journal · 2024Article
- Federated learning for medical image analysis: A survey.Pattern recognition · 2024Article
- Fast MRI Reconstruction Using Deep Learning-based Compressed Sensing: A Systematic Review.ArXiv · 2024Article
- Promoting fast MR imaging pipeline by full-stack AI.iScience · 2024Article
- Survey of Medical Applications of Federated Learning.Healthcare informatics research · 2024Article
- Modern Image-Guided Surgery: A Narrative Review of Medical Image Processing and Visualization.Sensors (Basel, Switzerland) · 2023Review
- FedFTN: Personalized federated learning with deep feature transformation network for multi-institutional low-count PET denoising.Medical image analysis · 2023Article
- Medical Imaging Applications of Federated Learning.Diagnostics (Basel, Switzerland) · 2023Review
- CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection.Proceedings. IEEE International Conference on Computer Vision · 2023Article
- Federated learning for medical imaging radiology.The British journal of radiology · 2023Review
- Influence of Data Distribution on Federated Learning Performance in Tumor Segmentation.Radiology. Artificial intelligence · 2023Article
- Peripheral and tumor-infiltrating immune cells are correlated with patient outcomes in ovarian cancer.Cancer medicine · 2023Article
- Federated End-to-End Unrolled Models for Magnetic Resonance Image Reconstruction.Bioengineering (Basel, Switzerland) · 2023Article
- Federated Transfer Learning for Low-dose PET Denoising: A Pilot Study with Simulated Heterogeneous Data.IEEE transactions on radiation and plasma medical sciences · 2023Article
- A Comprehensive Survey on Federated Learning Techniques for Healthcare Informatics.Computational intelligence and neuroscience · 2023Review
- Multiple sclerosis lesion segmentation: revisiting weighting mechanisms for federated learning.Frontiers in neuroscience · 2023Article
- New Generation Federated Learning.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based methods have been shown to produce superior performance on MR image reconstruction. However, these methods require large amounts of data which is difficult to collect and share due to the high cost of acquisition and medical data privacy regulations. In order to overcome this challenge, we propose a federated learning (FL) based solution in which we take advantage of the MR data available at different institutions while preserving patients' privacy. However, the generalizability of models trained with the FL setting can still be suboptimal due to domain shift, which results from the data collected at multiple institutions with different sensors, disease types, and acquisition protocols, etc. With the motivation of circumventing this challenge, we propose a cross-site modeling for MR image reconstruction in which the learned intermediate latent features among different source sites are aligned with the distribution of the latent features at the target site. Extensive experiments are conducted to provide various insights about FL for MR image reconstruction. Experimental results demonstrate that the proposed framework is a promising direction to utilize multi-institutional data without compromising patients' privacy for achieving improved MR image reconstruction. Our code is available at https://github.com/guopengf/FL-MRCM.
Identifiers
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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.