UniPET proposes a universal PET denoising network with style alignment network (SAN) and region-aware learning strategy (RALS) to handle varied dose reduction factors via domain generalization.
arXiv preprint arXiv:2503.05106
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GIF improves sample efficiency and convergence in high-dimensional HPO by using small-sample importance estimates to prioritize trials on high-impact hyperparameters.
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UniPET: a universal network for high-quality PET image denoising across varied dose reduction factors
UniPET proposes a universal PET denoising network with style alignment network (SAN) and region-aware learning strategy (RALS) to handle varied dose reduction factors via domain generalization.
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Importance-Aware Scheduling for High-Dimensional Hyperparameter Optimization
GIF improves sample efficiency and convergence in high-dimensional HPO by using small-sample importance estimates to prioritize trials on high-impact hyperparameters.