In this study, we introduce a generative model that can synthesize a large number of radiographical image/label pairs, and thus is asymptotically favorable to downstream activities such as segmentation in bio-medical image analysis. Denoising Diffusion Medical Model (DDMM), the proposed technique, can create realistic X-ray images and associated segmentations on a small number of annotated datasets as well as other massive unlabeled datasets with no supervision. Radiograph/segmentation pairs are generated jointly by the DDMM sampling process in probabilistic mode. As a result, a vanilla UNet that uses this data augmentation for segmentation task outperforms other similarly data-centric approaches.

翻译：在这项研究中，我们引入了一种生成模型，可以合成大量放射性图像/标签对，因此对生物医学图像分析中的分割等下游活动具有渐近有利的影响。提出了一种去噪扩散医学模型（DDMM），可以在少量注释数据集以及其他大量无标签数据集上创建逼真的X射线图像和相关分割。放射图/分割对由DDMM采样过程在概率模式下联合生成。因此，使用此数据增强的普通UNet在分割任务中优于其他类似数据中心方法。