A new progressive J-invariant self-supervised denoising method for LDCT that uses step-wise blind-spot enforcement and controlled noise injection outperforms prior self-supervised approaches on the Mayo dataset.
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HSANet uses Efficient Global Attention and hybrid upsampling in a Swin-based architecture to achieve better simultaneous denoising of low-dose PET/CT images than prior methods with a compact model.
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Progressive $\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising
A new progressive J-invariant self-supervised denoising method for LDCT that uses step-wise blind-spot enforcement and controlled noise injection outperforms prior self-supervised approaches on the Mayo dataset.
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Hybrid Swin Attention Networks for Simultaneously Low-Dose PET and CT Denoising
HSANet uses Efficient Global Attention and hybrid upsampling in a Swin-based architecture to achieve better simultaneous denoising of low-dose PET/CT images than prior methods with a compact model.