深度学习检测肺结节难点问题综述  

Review of Difficult Problems of Deep Learning to Detect Lung Nodules

作　　者：包强强 唐思源[2] 谷宇[1,3] BAO Qiangqiang;TANG Siyuan;GU Yu(School of Digtial and Intelligence Industry,Inner Mongolia University of Science and Technology,Baotou,Inner Mongolia 014010,China;School of Computer Science and Technology,Inner Mongolia University of Science and Technology Baotou Medical College,Baotou,Inner Mongolia 014040,China;Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing,School of Digtial and Intelligence Industry,Inner Mongolia University of Science and Technology,Baotou,Inner Mongolia 014010,China)

机构地区：[1]内蒙古科技大学数智产业学院,内蒙古包头014010 [2]内蒙古科技大学包头医学院计算机科学与技术学院,内蒙古包头014040 [3]内蒙古科技大学数智产业学院内蒙古自治区模式识别与智能图像处理重点实验室,内蒙古包头014010

出　　处：《计算机工程与应用》2024年第19期18-31,共14页Computer Engineering and Applications

基　　金：内蒙古自然科学基金(2021MS06026,2024LHMS06006);内蒙古自治区卫生健康委员会项目(202201395);包头市卫生健康科技计划项目(wsjkkj2022120);国家自然科学基金(62001255);中央引导地方科技发展资金项目(2021ZY0004);内蒙古自治区高等学校青年科技英才计划项目(NJYT23057);内蒙古科技大学基本科研业务费专项资金(019);内蒙古自治区大学生创新创业训练计划项目(s202310130004,s202410130004)。

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。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.

关 键 词：肺癌 肺结节检测 深度学习 计算机辅助检测系统 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]