DRMC: A Generalist Model with Dynamic Routing for Multi-Center PET Image Synthesis
This addresses domain shift issues in medical imaging for multi-center studies, offering a parameter-efficient solution, though it is incremental as it builds on existing generalist models with a novel routing strategy.
The paper tackles the problem of multi-center PET image synthesis, where domain shifts from different imaging systems/protocols degrade generalizability, and introduces a generalist model with dynamic routing to mitigate center interference, achieving excellent generalizability across centers.
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, \textit{i.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.