DARTS-searched feedback-augmented state space model for 3D pulmonary nodule classification

The automatic classification of pulmonary nodules is important for early cancer diagnosis. In this work, we aim to find the optimal state space model (SSM) for pulmonary nodule classification by leveraging Differentiable Architecture Search (DARTS). To achieve DARTS, we design the Differentiable State Space Supergraph (DSSS) to search for the optimal SSM. DSSS removes convolution, dimensionality expansion, and spatial attention around the SSM of Mamba while incorporating sampling-based multi-scale feature fusion. Additionally, anatomical scan provides multiple visual sequences of three-dimensional (3D) pulmonary nodules. During the supergraph search process, a knockout (KO) strategy is introduced to progressively remove low-probability candidate operations, thereby reducing the discrepancy between the continuous supergraph and the final discrete architecture while improving search efficiency. The discrete architecture obtained by DSSS is subsequently augmented with output feedback. By stacking the feedback-augmented cells, the final model, termed the Feedback-Augmented State Space Model (FASSM), is constructed. We conducted extensive experiments on the Lung Nodule Analysis 2016 (LUNA16) dataset, and the results show that the FASSM delivers outstanding performance. The architecture search was completed in less than six hours. The resulting FASSM achieved a mean classification accuracy of 93.22% across folds 5-9 with 8.59 M parameters. FASSM achieves a favorable balance between classification performance and model scale, with 8.59 M parameters and a computational cost of 20.41 GFLOPs. Related code and results have been released at: https://github.com/GuYuIMUST/DSSS-FASSM.