mlsanityData to 14 Oct 2026

DRMC: A Generalist Model with Dynamic Routing for Multi-Center PET Image Synthesis

arXiv 2307.05249 ↗First posted 11 Jul 2023eess.IV cs.CV cs.LGcited by 8

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Abstract (arXiv)

Multi-center positron emission tomography (PET) image synthesis aims at recovering low-dose PET images from multiple different centers. The generalizability of existing methods can still be suboptimal for a multi-center study due to domain shifts, which result from non-identical data distribution among centers with different imaging systems/protocols. While some approaches address domain shifts by training specialized models for each center, they are parameter inefficient and do not well exploit the shared knowledge across centers. To address this, we develop a generalist model that shares architecture and parameters across centers to utilize the shared knowledge. However, the generalist model can suffer from the center interference issue, textiti.e. the gradient directions of different centers can be inconsistent or even opposite owing to the non-identical data distribution. To mitigate such interference, we introduce a novel dynamic routing strategy with cross-layer connections that routes data from different centers to different experts. Experiments show that our generalist model with dynamic routing (DRMC) exhibits excellent generalizability across centers. Code and data are available at: https://github.com/Yaziwel/Multi-Center-PET-Image-Synthesis.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 13 of its 25 references that are in the map. First posted on arXiv 11 Jul 2023.

Builds on

13 of 25 references.

References · 13

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  4. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,358
  5. 2020Gradient Surgery for Multi-Task Learningcited by 1,256
  6. 2022OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Frameworkcited by 745
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  9. 2019Towards Universal Object Detection by Domain Attentioncited by 89
  10. 2021Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learningcited by 78
  11. 2022Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEscited by 58
  12. 2022Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image Denoisingcited by 19
1 more
  1. 20223D Segmentation Guided Style-based Generative Adversarial Networks for PET Synthesiscited by 13

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 1; 2026: 4.

8 papers.

  1. 2024Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKVcited by 36
  2. 2024All-In-One Medical Image Restoration via Task-Adaptive Routingcited by 30
  3. 2024Region Attention Transformer for Medical Image Restorationcited by 5
  4. 2025TAT: Task-Adaptive Transformer for All-in-One Medical Image Restorationcited by 3
  5. 2026MAP-Diff: Multi-Anchor Guided Diffusion for Progressive 3D Whole-Body Low-Dose PET Denoisingcited by 1
  6. 2026U-TTT: Towards Generalizable PET Image Denoising via Test-Time Trainingcited by 0
  7. 2026MARR: Decoupling Policy, Execution, and Calibration for All-in-One Medical Image Restorationcited by 0
  8. 2026UniH3: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restorationcited by 0