Diagnosis of oral squamous cell carcinoma (OSCC) currently relies heavily on the subjective experience of pathologists. This process is time-consuming and may have diagnostic inconsistencies due to subjective factors. This study aims to develop a novel deep learning model for accurate and interpretable classification of OSCC pathological images across different magnifications, while effectively addressing multi-center data heterogeneity and the difficulty in recognizing minority class samples. This study proposes a Pathology-prior Guided Multi-magnification Adaptive Fusion Network (PGMA-Net). The model incorporates pathological prior maps to direct attention toward crucial pathological features, and employs Pathology-Guided Adaptive Fusion Module (PGAFM) for multi-dimensional feature learning. Cross-Magnification Feature Alignment Regularization (CMFAR) is utilized to recognize images across magnifications, while Dynamic Weighted Class Balance (DWCB) improves learning from minority-class samples. Additionally, model integrates domain adaptation techniques to address multi-center data discrepancies. PGMA-Net was trained and validated on two different datasets, and compared with mainstream models. We verified the role of each core module through ablation experiments and visualized the model for interpretation. On the internal test set, PGMA-Net achieved good classification performance with an Area Under the receiver operating characteristic Curve (AUC) of 0.9740 and an accuracy of 0.9268, which was significantly superior to other comparative models. On the independent external test set, the model achieved an AUC of 0.9344 after domain adaptation, demonstrating robust cross-center generalization. Visualization results further demonstrated high alignment between the model’s decision regions and pathologists’ diagnostic bases. The PGMA-Net model achieves efficient and accurate classification of OSCC pathological images from different magnifications and multi-center sources. The model exhibits good generalization and interpretability, providing effective assistance to pathologists in OSCC diagnosis.
Cui et al. (Thu,) studied this question.