Our results indicate, through bit vector representations, that the network continues to refine class detectability with the last ReLU layer achieving better than 95% separation accuracy. Additionally, ...

With the widespread use of positron emission tomography (PET) crystals with greatly improved energy resolution (e.g., 11.5% with LYSO as compared to 20% with BGO) and of list-mode acquisitions, the ...

To address the above issue, the contrastive graph attention network (CGAT) is proposed, an innovative GNN method that integrates the graph attention network (GAT) and contrastive learning. First, two ...