A computer‐aided detection (CADe) scheme, relying on a large number of high‐quality images with annotations, could help radiologists effectively detect lung nodules. However, such medical data are generally difficult to obtain. To address this issue, this paper proposes a novel method based on a conditional generative adversarial network (CGAN) to generate new samples for data augmentation (DA). This method employs a 3D CGAN based on a squeeze‐and‐excitation mechanism and residual learning to synthesize realistic and diverse lung nodules in chest computed tomography (CT) images to improve the performance of the CADe system. To evaluate the proposed method, this paper uses synthetic samples for DA to train the lung nodule detection network. The results indicate that these synthetic samples, which cover a part of the data distribution unfilled by the original data, can boost the overall performance of the nodule detection network at fixed false positive (FP) rates.

中文翻译：

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基于3D条件生成对抗网络的合成医学图像增强用于肺结节检测

依靠大量带有注释的高质量图像的计算机辅助检测（CADe）方案可以帮助放射科医生有效地检测出肺结节。但是，这样的医学数据通常很难获得。为了解决这个问题，本文提出了一种基于条件生成对抗网络（CGAN）的新方法来生成用于数据增强（DA）的新样本。该方法采用基于挤压和激励机制以及残差学习的3D CGAN，在胸部计算机断层扫描（CT）图像中合成逼真的多样肺结节，以改善CADe系统的性能。为了评估所提出的方法，本文使用合成的DA样本来训练肺结节检测网络。结果表明，这些合成样品