DRMC: A Generalist Model with Dynamic Routing for Multi-Center PET Image
  Synthesis
Zhiwen Yang Yang Zhou Hui Zhang Bingzheng Wei Yubo Fan Yan Xu
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.