Abstract: Lung cancer is one of the deadliest cancers worldwide, and pulmonary nodules are an early manifestation of lung cancer. Detection models based on deep learning have gradually become an effective method to assist doctors in detecting pulmonary nodules due to their high detection accuracy and efficiency. However, current deep learning-based pulmonary nodule detection models still have shortcomings, and there are several challenging issues that need to be resolved. First, it addresses the issue of insufficient pulmonary nodule data and class imbalance during model training through transfer learning, GAN networks, semi-supervised learning, and unsupervised learning. Second, it enhances the model’s feature extraction capabilities to improve the sensitivity and accuracy of pulmonary nodule detection. Third, it improves the model’s capability to screen out false-positive pulmonary nodules, thereby reducing the false-positive rate. Fourth, it strengthens the model’s interpretability in detecting pulmonary nodules. Fifth, it solves the above four challenging issues using large model technology. Finally, it introduces the datasets and evaluation metrics required for the training and testing of detection models and discusses future optimization directions for pulmonary nodule detection.

Key words: lung cancer, lung nodule detection, deep learning, computer-aided detection system

摘要： 肺癌在全球范围内是致命性最高的癌症之一，肺结节是肺癌的早期表现形式，基于深度学习的肺结节检测模型因较高的检测准确率与效率逐渐成为辅助医生检测肺结节的有效方法。但是目前基于深度学习的肺结节检测模型仍有不足，一些重难点问题需要解决。第一，基于迁移学习、GAN网络、半监督学习与无监督学习解决模型训练时肺结节数据不足与类别不平衡问题；第二，增强模型特征提取能力提升对肺结节检测的敏感度与准确度；第三，提升模型假阳性肺结节筛查能力降低假阳性率；第四，加强模型检测肺结节的可解释能力；第五，基于大模型技术解决以上4个难点问题。最后，介绍检测模型训练与测试所需的数据集与评价指标并对未来肺结节检测优化方向进行讨论。

关键词: 肺癌, 肺结节检测, 深度学习, 计算机辅助检测系统