关键词: 困难气道识别, 多视图学习, 数据增强, 类别数量不平衡, 特征融合, 注意力机制

Abstract: Difficult Airway (DA) is a critical preoperative risk factor in clinical surgery, yet its accurate identification faces numerous challenges, such as small dataset size, severe class imbalance, and insufficient single-view recognition capability. To address these limitations, a multi-view DA identification model, DRG-MV-Net (Discriminative Region Guided Multi-View Net), was proposed. In the first stage, the Discriminative Region Guided Module (DRGM) was employed to automatically detect and emphasize key discriminative regions in facial views using Class Activation Mapping (CAM), generating two types of augmented images with specific features. In the second stage, features for each view were extracted using ResNet-18 with the integrated Dilated Convolution Block Attention Module(D-CBAM) as the backbone, and multi-view feature integration was performed via the Multi-View Cross Fusion Module (MCFM). Focal Loss and stratified hybrid sampling were utilized to mitigate the class imbalance problem. Evaluation on the constructed clinical dataset demonstrated that the proposed model achieved a G-Mean of 77.22%, an F1-Score of 43.88%, a Matthews Correlation Coefficient (MCC) of 38.73%, and an Area Under the receiver operating Characteristic curve (AUC) of 0.7407. Compared with the recent method MCE-Net (Multi-view Contrastive representation prior and Ensemble classification Network), the G-Mean, F1-Score, and MCC improved by 2.41, 2.34, and 3.41 percentage points, respectively. Compared with the baseline model ResNet-18, these metrics improved by 4.85, 6.85, and 8.25 percentage points, respectively, verifying the effectiveness of the proposed method in DA identification on small, unbalanced datasets and providing new insights.

Key words: difficult airway recognition, multi-view learning, data augmentation, imbalanced number of categories, feature fusion, attention mechanism