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.
Tenenholtz, Jameson K
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Off-the-shelf image diffusion models can be repurposed to create synthetic structured data capable of inducing ground truth drift in machine pipelines.
SPADE-LDM conditional synthesis from composite semantic masks produces realistic 3D LGE MRI that raises LA cavity Dice from 0.908 to 0.936.
citing papers explorer
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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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Repurposing Image Diffusion Models for Adversarial Synthetic Structured Data: A Case Study of Ground Truth Drift
Off-the-shelf image diffusion models can be repurposed to create synthetic structured data capable of inducing ground truth drift in machine pipelines.
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3D Conditional Image Synthesis of Left Atrial LGE MRI from Composite Semantic Masks
SPADE-LDM conditional synthesis from composite semantic masks produces realistic 3D LGE MRI that raises LA cavity Dice from 0.908 to 0.936.