Explainable hybrid CNN-transformer with self-supervised learning for structural analysis of paranasal sinus CT.

Introduction: The process of precise structural evaluation for paranasal sinuses based on CT scan data establishes a foundation for medical professionals to assess human anatomical variations, supporting the diagnosis and treatment of ear, nose, and throat (ENT) conditions. Existing deep learning methods face difficulties in analyzing complex sinus structures due to limited annotated datasets and lack of clinical interpretability. Methods: This study presents an explainable hybrid CNN-Transformer framework incorporating a self-supervised 3D convolutional autoencoder to perform structural analysis of paranasal sinus CT volumes. The framework is evaluated on the multi-institutional CT-SCOPE dataset, which contains diverse scan data from different hospitals and CT scanner models. The proposed approach combines anatomical segmentation to generate precise boundaries with residual-based structural representation learning for anomaly detection without requiring pathology labels. Results: The hybrid segmentation model achieves high anatomical fidelity, producing Dice similarity coefficients above 0.83 across all four sinus regions, including maxillary, ethmoid, frontal, and sphenoid sinuses. The architecture integrates convolutional feature extraction with Transformer-based contextual modeling to capture both fine structural details and global anatomical context. The self-supervised autoencoder generates reconstruction residual maps that highlight structural deviations from standard osseous patterns. Grad-CAM-based interpretability analysis demonstrates that the model focuses on clinically relevant regions such as sinus walls and ethmoidal partitions. Discussion: The integration of accurate segmentation, residual-based structural representation learning, and explainable attention mechanisms provides a comprehensive framework for anatomical analysis of paranasal sinus CT images. This approach establishes a foundation for future pathology-aware clinical modeling and supports the development of reliable AI-assisted diagnostic systems.