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

arXiv: https://arxiv.org/abs/2307.05249 · HTML: https://mlsanity.com/paper/2307.05249/

## 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 (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Gradient Surgery for Multi-Task Learning](https://mlsanity.com/paper/2001.06782/) (2020)
- [OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework](https://mlsanity.com/paper/2202.03052/) (2022)
- [Dynamic Neural Networks: A Survey](https://mlsanity.com/paper/2102.04906/) (2021)
- [Uni-Perceiver: Pre-training Unified Architecture for Generic Perception for Zero-shot and Few-shot Tasks](https://mlsanity.com/paper/2112.01522/) (2021)
- [Towards Universal Object Detection by Domain Attention](https://mlsanity.com/paper/1904.04402/) (2019)
- [Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning](https://mlsanity.com/paper/2103.02148/) (2021)
- [Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs](https://mlsanity.com/paper/2206.04674/) (2022)
- [Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image Denoising](https://mlsanity.com/paper/2209.03300/) (2022)
- [3D Segmentation Guided Style-based Generative Adversarial Networks for PET Synthesis](https://mlsanity.com/paper/2205.08887/) (2022)

## Built on by (most cited first, 8 of 8)

- [Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKV](https://mlsanity.com/paper/2407.11087/) (2024)
- [All-In-One Medical Image Restoration via Task-Adaptive Routing](https://mlsanity.com/paper/2405.19769/) (2024)
- [Region Attention Transformer for Medical Image Restoration](https://mlsanity.com/paper/2407.09268/) (2024)
- [TAT: Task-Adaptive Transformer for All-in-One Medical Image Restoration](https://mlsanity.com/paper/2512.14550/) (2025)
- [MAP-Diff: Multi-Anchor Guided Diffusion for Progressive 3D Whole-Body Low-Dose PET Denoising](https://mlsanity.com/paper/2603.02012/) (2026)
- [U-TTT: Towards Generalizable PET Image Denoising via Test-Time Training](https://mlsanity.com/paper/2606.11032/) (2026)
- [MARR: Decoupling Policy, Execution, and Calibration for All-in-One Medical Image Restoration](https://mlsanity.com/paper/2609.06645/) (2026)
- [UniH3: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration](https://mlsanity.com/paper/2609.11156/) (2026)

## 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.