A Data Augmentation Method and the Embedding Mechanism for Detection of Pulmonary Nodules on Small Samples

Abstract

Lung Computed Tomography (CT) screening for pulmonary nodules provides an effective method for early diagnosis. The deep-learning-based computer-aided detection (CAD) system effectively identifies and precisely localizes suspicious pulmonary nodules in CT images, thereby significantly enhancing the accuracy and efficiency of CT diagnosis. In the medical field, the availability of medical data is limited, and research using small samples is of practical significance. By studying the data augmentation technology based on the generative model under the condition of small samples, and refining the model structure through the embedding mechanism, the accuracy and robustness of the deep learning model are explored. A 3D pixel-level statistical algorithm is proposed for the generation of pulmonary nodules. By combining simulated pulmonary nodules with healthy lung tissue, we can generate new samples of pulmonary nodules. The embedding mechanism is designed to enhance the comprehension of pixel meanings in pulmonary nodule samples by introducing latent variables. The results of the 3DVNET model with the augmentation method for pulmonary nodule detection under small sample conditions demonstrate that the proposed data augmentation method outperforms the method based on a generative adversarial network (GAN) framework, and the embedding mechanism for pulmonary nodules detection shows a significant improvement in accuracy. Conclusion: the proposed data augmentation method and embedding mechanism demonstrate significant potential in enhancing the accuracy and robustness of the model, thereby facilitating their application to various common imaging diagnostic tasks, and research using small samples is of practical significance.