Lung Nodule Image Synthesis Driven by Two-Stage Generative Adversarial Networks

• URL: http://arxiv.org/abs/2602.02171v1
• Date: Mon, 02 Feb 2026 14:45:11 GMT
• Title: Lung Nodule Image Synthesis Driven by Two-Stage Generative Adversarial Networks
• Authors: Lu Cao, Xiquan He, Junying Zeng, Chaoyun Mai, Min Luo,
• Abstract summary: We propose a two-stage generative adversarial network (TSGAN) to enhance the diversity and spatial controllability of synthetic data.<n>In the first stage, StyleGAN is used to generate semantic segmentation mask images, encoding lung nodules and tissue backgrounds.<n>The second stage uses the DL-Pix2Pix model to translate the mask map into CT images, employing local importance attention to capture local features.<n>Compared to the original dataset, the accuracy improved by 4.6% and mAP by 4% on the LUNA16 dataset.
• Score: 3.735262910545115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The limited sample size and insufficient diversity of lung nodule CT datasets severely restrict the performance and generalization ability of detection models. Existing methods generate images with insufficient diversity and controllability, suffering from issues such as monotonous texture features and distorted anatomical structures. Therefore, we propose a two-stage generative adversarial network (TSGAN) to enhance the diversity and spatial controllability of synthetic data by decoupling the morphological structure and texture features of lung nodules. In the first stage, StyleGAN is used to generate semantic segmentation mask images, encoding lung nodules and tissue backgrounds to control the anatomical structure of lung nodule images; The second stage uses the DL-Pix2Pix model to translate the mask map into CT images, employing local importance attention to capture local features, while utilizing dynamic weight multi-head window attention to enhance the modeling capability of lung nodule texture and background. Compared to the original dataset, the accuracy improved by 4.6% and mAP by 4% on the LUNA16 dataset. Experimental results demonstrate that TSGAN can enhance the quality of synthetic images and the performance of detection models.

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