Medical imaging applications are highly specialized in terms of human anatomy, pathology, and imaging domains. Therefore, annotated training datasets for training deep learning applications in medical imaging not only need to be highly accurate but also diverse and large enough to encompass almost all plausible examples with respect to those specifications. We argue that achieving this goal can be facilitated through a controlled generation framework for synthetic images with annotations, requiring multiple conditional specifications as input to provide control. We employ a Denoising Diffusion Probabilistic Model (DDPM) to train a large-scale generative model in the lung CT domain and expand upon a classifier-free sampling strategy to showcase one such generation framework. We show that our approach can produce annotated lung CT images that can faithfully represent anatomy, convincingly fooling experts into perceiving them as real. Our experiments demonstrate that controlled generative frameworks of this nature can surpass nearly every state-of-the-art image generative model in achieving anatomical consistency in generated medical images when trained on comparable large medical datasets.

本研究针对医学图像合成中的数据集多样性和准确性问题，提出一种基于多条件输入的合成图像生成框架。通过使用去噪扩散概率模型（DDPM），我们展示了该框架能够生成高度准确的肺部CT图像，能够以假乱真地欺骗专家，推动了医学图像生成领域的发展。

多条件去噪扩散概率模型（mDDPM）用于医学图像合成