CRAFT adapts diffusion models to medical images via clinical reward alignment from LLMs and VLMs, improving alignment scores and cutting low-quality generations by 20.4% on average across modalities.
MAISI-v2: Accelerated 3d high-resolution medical image synthesis with rectified flow and region-specific contrastive loss
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DiffKT3D transfers priors from video diffusion models to 3D radiotherapy dose prediction via modality-specific embeddings and clinically guided RL, reducing voxel MAE from 2.07 to 1.93 and claiming SOTA over the GDP-HMM challenge winner.
Training-free guidance of a pretrained 3D rectified flow model enables weakly-supervised lung nodule segmentation using only image-level labels and produces improved results on the LUNA16 dataset.
citing papers explorer
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CRAFT: Clinical Reward-Aligned Finetuning for Medical Image Synthesis
CRAFT adapts diffusion models to medical images via clinical reward alignment from LLMs and VLMs, improving alignment scores and cutting low-quality generations by 20.4% on average across modalities.
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Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study
DiffKT3D transfers priors from video diffusion models to 3D radiotherapy dose prediction via modality-specific embeddings and clinically guided RL, reducing voxel MAE from 2.07 to 1.93 and claiming SOTA over the GDP-HMM challenge winner.
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Weakly-Supervised Lung Nodule Segmentation via Training-Free Guidance of 3D Rectified Flow
Training-free guidance of a pretrained 3D rectified flow model enables weakly-supervised lung nodule segmentation using only image-level labels and produces improved results on the LUNA16 dataset.