Abstract
Early screening of lung nodules is mainly done manually by reading the patient's lung CT. This approach is time-consuming laborious and prone to leakage and misdiagnosis. Current methods for lung nodule detection face limitations such as the high cost of obtaining large-scale, high-quality annotated datasets and poor robustness when dealing with data of varying quality. The challenges include accurately detecting small and irregular nodules, as well as ensuring model generalization across different data sources. Therefore, this paper proposes a lung nodule detection model based on semi-supervised learning and knowledge distillation (SSLKD-UNet). In this paper, a feature encoder with a hybrid architecture of CNN and Transformer is designed to fully extract the features of lung nodule images, and at the same time, a distillation training strategy is designed in this paper, which uses the teacher model to instruct the student model to learn the more relevant features to nodule regions in the CT images and, and finally, this paper applies the rough annotation of the lung nodules to the LUNA16 and LC183 dataset with the help of semi-supervised learning idea, and completes the model with the accurate annotation of lung nodules. Combined with the accurate lung nodule annotation to complete the model training process. Further experiments show that the model proposed in this paper can utilize a small amount of inexpensive and easy-to-obtain coarse-grained annotations of pulmonary nodules for training under the guidance of semi-supervised learning and knowledge distillation training strategies, which means inaccurate annotations or incomplete information annotations, e.g., using nodule coordinates instead of pixel-level segmentation masks, and realize the early recognition of lung nodules. The segmentation results further corroborates the model's efficacy, with SSLKD-UNet demonstrating superior delineation of lung nodules, even in cases with complex anatomical structures and varying nodule sizes.