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Image Synthesis with Disentangled Attributes for Chest X-Ray Nodule Augmentation and Detection by Shen, Zhenrong; Ouyang, Xi; Xiao, Bin; Cheng, Jie-Zhi; Wang, Qian; Shen, Dinggang is a scholarly article available to read on EtoBox.

What is Image Synthesis with Disentangled Attributes for Chest X-Ray Nodule Augmentation and Detection about?

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. Moreov

Author

Shen, Zhenrong; Ouyang, Xi; Xiao, Bin; Cheng, Jie-Zhi; Wang, Qian; Shen, Dinggang

Published

2022

Language

EN