LiFT factorizes 3D medical volume synthesis into per-slice 2D generation and inter-slice trajectory learning, using a tri-planar drifting loss for unconditional coherence and a z-context mixer for paired translation tasks.
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A selective inference framework is proposed to provide p-values controlling false positive rates for diffusion-based anomaly localization in images.
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LiFT: Lifted Inter-slice Feature Trajectories for 3D Image Generation from 2D Generators
LiFT factorizes 3D medical volume synthesis into per-slice 2D generation and inter-slice trajectory learning, using a tri-planar drifting loss for unconditional coherence and a z-context mixer for paired translation tasks.
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Statistical Test for Diffusion-Based Anomaly Localization via Selective Inference
A selective inference framework is proposed to provide p-values controlling false positive rates for diffusion-based anomaly localization in images.