用最优传输流匹配加速医学图像生成，又快又准。
Flow Matching for Medical Image Synthesis: Bridging the Gap Between Speed and Quality
- 通过最优传输流匹配构建更直接的分布映射，缩短生成路径。
- 推理速度显著提升，同时图像质量优于传统扩散模型。
- 支持多模态、多维度医学图像生成，适配临床研究与数据增强。
深度学习在医疗领域表现优异，但依赖大规模高质量数据，而数据获取受限于隐私与标注成本。生成模型如扩散模型可合成医学图像，但推理时间过长制约实际应用。本文提出基于最优传输的流匹配方法，通过构建源分布与目标分布间的更直接映射，大幅降低推理时间，同时保持并进一步提升输出质量。该方法具有高度适应性，支持多种医学成像模态、条件机制（如类别标签、分割掩码）及空间维度（2D/3D）。除图像生成外，还可用于图像增强等任务。实验表明该框架高效且通用，推动医学影像应用发展。代码与合成数据集已开源：https://github.com/milad1378yz/MOTFM。
原文摘要 · Abstract (English)
Deep learning models have emerged as a powerful tool for various medical applications. However, their success depends on large, high-quality datasets that are challenging to obtain due to privacy concerns and costly annotation. Generative models, such as diffusion models, offer a potential solution by synthesizing medical images, but their practical adoption is hindered by long inference times. In this paper, we propose the use of an optimal transport flow matching approach to accelerate image generation. By introducing a straighter mapping between the source and target distribution, our method significantly reduces inference time while preserving and further enhancing the quality of the outputs. Furthermore, this approach is highly adaptable, supporting various medical imaging modalities, conditioning mechanisms (such as class labels and masks), and different spatial dimensions, including 2D and 3D. Beyond image generation, it can also be applied to related tasks such as image enhancement. Our results demonstrate the efficiency and versatility of this framework, making it a promising advancement for medical imaging applications. Code with checkpoints and a synthetic dataset (beneficial for classification and segmentation) is now available on: https://github.com/milad1378yz/MOTFM.
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