通过特征对齐提升医学影像合成质量，生成更真实多样。
Improved Generation of Synthetic Imaging Data Using Feature-Aligned Diffusion
- 让扩散模型中间特征匹配专家网络输出，实现更精准合成
- 生成准确率提升9%，SSIM多样性提高约0.12
- 可无缝集成现有流程，适合医学图像生成研究者
合成数据生成是机器学习在医学影像领域的重要应用。尽管现有方法已成功使用微调的扩散模型合成医学图像，我们通过特征对齐扩散探索了该流程的改进潜力。该方法将扩散模型的中间特征与专家网络的输出特征对齐，初步结果显示生成准确率提升9%，SSIM多样性提升约0.12。该方法与现有技术具有协同效应，可轻松融入扩散训练流程以进一步提升性能。代码已公开于 <https://github.com/lnairGT/Feature-Aligned-Diffusion>。
原文摘要 · Abstract (English)
Synthetic data generation is an important application of machine learning in the field of medical imaging. While existing approaches have successfully applied fine-tuned diffusion models for synthesizing medical images, we explore potential improvements to this pipeline through feature-aligned diffusion. Our approach aligns intermediate features of the diffusion model to the output features of an expert, and our preliminary findings show an improvement of 9% in generation accuracy and ~0.12 in SSIM diversity. Our approach is also synergistic with existing methods, and easily integrated into diffusion training pipelines for improvements. We make our code available at \url{https://github.com/lnairGT/Feature-Aligned-Diffusion}.
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