  Lung nodule detection in chest X-ray (CXR) images is common to early
screening of lung cancers. Deep-learning-based Computer-Assisted Diagnosis
(CAD) systems can support radiologists for nodule screening in CXR. However, it
requires large-scale and diverse medical data with high-quality annotations to
train such robust and accurate CADs. To alleviate the limited availability of
such datasets, lung nodule synthesis methods are proposed for the sake of data
augmentation. Nevertheless, previous methods lack the ability to generate
nodules that are realistic with the size attribute desired by the detector. To
address this issue, we introduce a novel lung nodule synthesis framework in
this paper, which decomposes nodule attributes into three main aspects
including shape, size, and texture, respectively. A GAN-based Shape Generator
firstly models nodule shapes by generating diverse shape masks. The following
Size Modulation then enables quantitative control on the diameters of the
generated nodule shapes in pixel-level granularity. A coarse-to-fine gated
convolutional Texture Generator finally synthesizes visually plausible nodule
textures conditioned on the modulated shape masks. Moreover, we propose to
synthesize nodule CXR images by controlling the disentangled nodule attributes
for data augmentation, in order to better compensate for the nodules that are
easily missed in the detection task. Our experiments demonstrate the enhanced
image quality, diversity, and controllability of the proposed lung nodule
synthesis framework. We also validate the effectiveness of our data
augmentation on greatly improving nodule detection performance.
