mlsanityData to 7 Oct 2026

Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imaging

arXiv 2206.03709 ↗First posted 8 Jun 2022eess.IV cs.CVcited by 14

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Abstract (arXiv)

Computed tomography (CT) is of great importance in clinical practice due to its powerful ability to provide patients' anatomical information without any invasive inspection, but its potential radiation risk is raising people's concerns. Deep learning-based methods are considered promising in CT reconstruction, but these network models are usually trained with the measured data obtained from specific scanning protocol and need to centralizedly collect large amounts of data, which will lead to serious data domain shift, and privacy concerns. To relieve these problems, in this paper, we propose a hypernetwork-based federated learning method for personalized CT imaging, dubbed as HyperFed. The basic assumption of HyperFed is that the optimization problem for each institution can be divided into two parts: the local data adaption problem and the global CT imaging problem, which are implemented by an institution-specific hypernetwork and a global-sharing imaging network, respectively. The purpose of global-sharing imaging network is to learn stable and effective common features from different institutions. The institution-specific hypernetwork is carefully designed to obtain hyperparameters to condition the global-sharing imaging network for personalized local CT reconstruction. Experiments show that HyperFed achieves competitive performance in CT reconstruction compared with several other state-of-the-art methods. It is believed as a promising direction to improve CT imaging quality and achieve personalized demands of different institutions or scanners without privacy data sharing. The codes will be released at https://github.com/Zi-YuanYang/HyperFed.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 34 of its 79 references that are in the map. First posted on arXiv 8 Jun 2022.

Builds on

34 of 79 references.

References · 34

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22 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 5; 2025: 5; 2026: 1.

14 papers.

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  1. 2023Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verificationcited by 6
  2. 2025Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language Modelcited by 6
  3. 2024FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantificationcited by 4
  4. 2024Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systemscited by 3
  5. 2024Cross-Chirality Palmprint Verification: Left is Right for the Right Palmprintcited by 2
  6. 2025Federated Learning for Large Models in Medical Imaging: A Comprehensive Reviewcited by 2
  7. 2024Dual-Domain CLIP-Assisted Residual Optimization Perception Model for Metal Artifact Reductioncited by 1
  8. 2023Privacy-Preserving Encrypted Low-Dose CT Denoisingcited by 0
  9. 2023Energizing Federated Learning via Filter-Aware Attentioncited by 0
  10. 2024Double Banking on Knowledge: Customized Modulation and Prototypes for Multi-Modality Semi-supervised Medical Image Segmentationcited by 0
  11. 2025Federated Low-Rank Tensor Estimation for Multimodal Image Reconstructioncited by 0
  12. 2025FedPalm: A General Federated Learning Framework for Closed- and Open-Set Palmprint Verificationcited by 0
2 more
  1. 2025Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitionscited by 0
  2. 2026Projection Guided Personalized Federated Learning for Low Dose CT Denoisingcited by 0