关键词: 深度学习, CT图像, 候选结节检测, 卷积神经网络

Abstract:

Lung cancer is the most deadly cancer in the world. Detection of lung nodules by chest CT images is of great significance for the early diagnosis and treatment of lung cancer. In order to reduce the workload of radiologists and at the same time reduce the rate of misdiagnosis and missed diagnosis, researchers have proposed a Computer-Aided Detection（CAD） system to assist radiologists in detecting and diagnosing pulmonary nodules. Researchers are currently experimenting with different deep learning techniques to improve the performance of computer-aided diagnostic systems in CT-based lung cancer screening. This work reviews the current typical deep learning algorithms and frameworks for CAD systems for lung cancer detection. It mainly introduces six aspects：data set introduction, 2D deep learning methods, 3D deep learning methods, data imbalance processing, model training methods and model interpretability. Finally, the main characteristics and algorithm performance of each method are comprehensively compared and analyzed, and how to improve the nodule detection performance is prospected.

Key words: deep learning, CT images, candidate nodule detection, convolutional neural network