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• W4387225725 abstract "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, 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 ." @default.
• W4387225725 created "2023-10-01" @default.
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• W4387225725 date "2023-01-01" @default.
• W4387225725 modified "2023-10-01" @default.
• W4387225725 title "DRMC: A Generalist Model with Dynamic Routing for Multi-center PET Image Synthesis" @default.
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• W4387225725 doi "https://doi.org/10.1007/978-3-031-43898-1_4" @default.
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