DSANet: 3D Deformable Slice-Aware Network with Adaptive Slice Grouping for Robust Pulmonary Nodule Detection

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Lung cancer remains a leading cause of cancer-related mortality, with early detection of pulmonary nodules critical for improving patient outcomes. Although deep learning models have shown promise in nodule detection using low-dose CT scans, existing methods struggle with generalization across diverse nodule morphologies, particularly for micronodules (≤10mm). To address these challenges, we propose DSANet, a novel 3D Deformable Slice-Aware Network featuring an adaptive deformable slice grouped (DSG) module. The DSG module dynamically adjusts slice grouping strategies and attention weights based on CT image features, enhancing 3D spatial feature extraction for nodules of varying sizes and shapes. We evaluated DSANet on both our large-scale ChestCT2025 dataset (1,000 scans, 1,465 nodules) and the public LUNA2016 dataset. Experimental results demonstrate that DSANet outperforms state-of-the-art methods in key metrics, with significant improvements in small-nodule detection. Ablation studies confirm the critical role of the DSG module in increasing the detection accuracy. Our approach offers a robust solution for early lung cancer diagnosis, particularly in challenging micronodule cases. The code is available at https://github.com/czy020202/DSANet.

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