Cross-region feature fusion of global and local area for subtype classification prediction in cervical tumour

To develop a cross-regional feature fusion model for the classification of cervical tumour subtypes using non-invasive magnetic resonance imaging (MRI), we aimed to explore feature representation in both global and local areas, and to compare their effect on predictive performance. This retrospective study included 100 patients with cervical cancer, approved by the Ethical Review Board Committee. Self-supervised learning-based global features were fused with local features for subtype classification modelling. Global features were extracted from the bottleneck of our 3D autoencoder network, while local features were derived based on a radiomics tool. Utilizing the global and local features, the classification model is based on machine learning algorithms to predict two subtypes with pathologically confirmed cervical squamous cell carcinoma and adenocarcinoma. Comparison performance was accessed using area under the curve (AUC), sensitivity, specificity, F1 score, and precision. The cross-regional feature fusion model showed the best performance (accuracy: 0.95 vs 0.65 in the fusion model and global model) by the support vector machines (SVM) classifier. Even when applied to axial slices with various classification methods, the fusion model consistently yields the best results. Our approach preliminary evidence suggests that the fusion of global and local features provides a significant advantage in the clinical diagnosis of cervical cancer subtypes, warranting further investigation and potential application in cervical cancer diagnosis.