深度学习驱动的CT影像肺结节检测：挑战、进展和展望

Deep Learning-driven Pulmonary Nodule Detection from CT Images: Challenges, Current Status and Future Directions

摘要: 基于CT影像进行肺结节自动检测可以显著提高肺癌诊治效果。该文结合CT影像和肺结节的特点，总结了基于各种深度学习模型的CT影像肺结节检测面临的挑战和近期研究进展，重点分析了标志性成果和它们的优缺点。结合当前肺结节检测应用的现状，对未来更好地应用和改进基于深度学习的CT影像肺结节检测技术提出了展望。

Abstract: Automatic detection of pulmonary nodule based on CT images can significantly improve the diagnosis and treatment of lung cancer. Based on the characteristics of CT image and pulmonary nodule, this study summarizes the challenges and recent progresses of CT image-based pulmonary nodule detection using various deep learning models. The study focuses on the review of major research developments by investigating their technical details, strengths and shortcomings. In light of the current application status of pulmonary nodule detection, a research agenda that aims to better apply and improve deep learningdriven pulmonary nodule detection technologies was given in this study.