The scarcity of publicly available medical imaging data limits the development of effective AI models. This work proposes a memory-efficient patch-wise denoising diffusion probabilistic model (DDPM) for generating synthetic medical images, focusing on CT scans with lung nodules. Our approach generates high-utility synthetic images with nodule segmentation while efficiently managing memory constraints, enabling the creation of training datasets. We evaluate the method in two scenarios: training a segmentation model exclusively on synthetic data, and augmenting real-world training data with synthetic images. In the first case, models trained solely on synthetic data achieve Dice scores comparable to those trained on real-world data benchmarks. In the second case, augmenting real-world data with synthetic images significantly improves segmentation performance. The generated images demonstrate their potential to enhance medical image datasets in scenarios with limited real-world data.

本研究解决了公共医疗影像数据稀缺问题，提出了一种内存高效的补丁去噪扩散概率模型（DDPM）用于生成合成医疗图像，特别关注肺结节的CT扫描。关键发现表明，所生成的合成图像在训练数据集的创建中表现出显著提升潜力，有助于提高模型的分割性能。

评估内存高效的医疗图像生成的效用：肺结节分割的研究