深度学习检测肺结节难点问题综述OA北大核心CSTPCD

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 de-tecting pulmonary nodules due to their high detection accuracy and efficiency.However,current deep learning-based pul-monary 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 trans-fer learning,GAN networks,semi-supervised learning,and unsupervised learning.Second,it enhances the model's fea-ture 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 test-ing of detection models and discusses future optimization directions for pulmonary nodule detection.