  Lung cancer has been one of the leading causes of cancer-related deaths
worldwide for years. With the emergence of deep learning, computer-assisted
diagnosis (CAD) models based on learning algorithms can accelerate the nodule
screening process, providing valuable assistance to radiologists in their daily
clinical workflows. However, developing such robust and accurate models often
requires large-scale and diverse medical datasets with high-quality
annotations. Generating synthetic data provides a pathway for augmenting
datasets at a larger scale. Therefore, in this paper, we explore the use of
Semantic Diffusion Mod- els (SDM) to generate high-fidelity pulmonary CT images
from segmentation maps. We utilize annotation information from the LUNA16
dataset to create paired CT images and masks, and assess the quality of the
generated images using the Frechet Inception Distance (FID), as well as on two
common clinical downstream tasks: nodule detection and nodule localization.
Achieving improvements of 3.96% for detection accuracy and 8.50% for AP50 in
nodule localization task, respectively, demonstrates the feasibility of the
approach.
