A novel high-precision bilevel optimization method for 3D pulmonary nodule classification

Background and objective: Classification of pulmonary nodules is important for the early diagnosis of lung cancer; however, the manual design of classification models requires substantial expert effort. To automate the model design process, we propose a neural architecture search with high-precision bilevel optimization (NAS-HBO) that directly searches for the optimal network on three-dimensional (3D) images. Methods: We propose a novel high-precision bilevel optimization method (HBOM) to search for an optimal 3D pulmonary nodule classification model. We employed memory optimization techniques with a partially decoupled operation-weighting method to reduce the memory overhead while maintaining path selection stability. Additionally, we introduce a novel maintaining receptive field criterion (MRFC) within the NAS-HBO framework. MRFC narrows the search space by selecting and expanding the 3D Mobile Inverted Residual Bottleneck Block (3D-MBconv) operation based on previous receptive fields, thereby enhancing the scalability and practical application capabilities of NAS-HBO in terms of model complexity and performance. Results: In this study, 888 CT images, including 554 benign and 450 malignant nodules, were obtained from the LIDC-IDRI dataset. The results showed that NAS-HBO achieved an impressive accuracy of 91.51 % after less than 6 h of searching, utilizing a mere 12.79 M parameters. Conclusion: The proposed NAS-HBO method effectively automates the design of 3D pulmonary nodule classification models, achieving impressive accuracy with efficient parameters. By incorporating the HBOM and MRFC techniques, we demonstrated enhanced accuracy and scalability in model optimization for early lung cancer diagnosis. The related codes and results have been released at https://github.com/GuYuIMUST/NAS-HBO.