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：在本研究中，我们提出了一种生成模型，能够合成大量放射影像/标签对，从而在生物医学图像分析中的分割等下游任务中渐近地展现出优势。所提出的去噪扩散医学模型（DDMM）可在少量标注数据集以及大量无标注数据集上无需监督地生成逼真的X光图像及其对应的分割结果。DDMM的采样过程以概率模式联合生成放射影像/分割对。实验结果表明，采用此数据增强方法的普通UNet在分割任务上优于其他类似的数据驱动方法。