Class-Aware Multi-window Adversarial Lung Nodule Synthesis Conditioned on Semantic Features

Abstract

Nodule CT image synthesis is effective as a data augmentation method for deep learning tasks about lung nodules. To advance the realistic malignant/benign lung nodule synthesis, the conditional Generative Adversarial Networks have been widely adopted. In this paper, we argue about an issue in the existing technique for class-aware nodule synthesis: the class-aware controllability of semantic features. To address this issue, we propose a adversarial lung nodule synthesis framework based on conditional Generative Adversarial Networks and class-aware multi-window semantic feature learning. By learning semantic features from multi-window CT images, our framework can generate realistic nodule CT images, and has better controllability of class-aware nodule features. Our framework provides a new perspective for nodule CT image synthesis that has never been noticed before. We train our framework on the public dataset LIDC-IDRI. Our framework improves the malignancy prediction F1 score by more than 3% and shows promising results as a solution for lung nodule augmentation. The source code can be found at https://github.com/qiuliwang/CA-MW-Adversarial-Synthesis.