Work

Omni-Swin3D: A radiomics-aware architecture for robust lung cancer diagnosis

Year: 2026

Type: article

Abstract: Benign–malignant classification of pulmonary nodules from chest computed tomography (CT) is essential for early lung cancer screening and for reducing unnecessary biopsies and follow-up examinations, yet it remains challenging because malignancy cues are subtle, heterogeneous, and distributed across 3D anatomy at multiple scales. We propose Omni-Swin3D, a radiomics-aware 3D hierarchical Transformer that preserves fine-grained texture and boundary evidence while enabling efficient global context modeling. Omni-Swin3D integrates a multi-scale radiomics encoder to capture complementary density/morphology patterns before tokenization, equips a four-stage Omni-Swin backbone with Adaptive Dual-Path Top- k Attention for sparse global aggregation with local continuity compensation, and adopts Adaptive Radiomics-Preserving Downsampling to mitigate discriminative information loss during hierarchical resolution reduction. A radiomics-aware prediction head further consolidates global average/max statistics via channel recalibration, and the model is optimized end-to-end with weighted cross-entropy to address class imbalance. Experiments on a public benchmark constructed from LUNA16/LIDC-IDRI demonstrate that Omni-Swin3D achieves 90 . 29 ± 1 . 45 % accuracy (balanced accuracy), 89 . 74 ± 1 . 82 % recall, 90 . 88 ± 1 . 34 % specificity, and an AUC of 0 . 980 ± 0 . 008 under patient-level 10-fold cross-validation, while ablations confirm the complementary contributions and efficiency benefits of each component. Overall, these results indicate that explicitly preserving multi-scale radiomics evidence within an efficient 3D attention hierarchy yields robust malignancy discrimination. Future work will extend Omni-Swin3D toward multi-task prediction across multiple datasets by jointly modeling malignancy and additional clinically relevant targets.

Source: Alexandria Engineering Journal

DOI: 10.1016/j.aej.2026.04.007