Explainable 3D Swin Transformer for Tumor Mass Presence Classification and Quantitative Localization in Pancreatic CT: A Comparative Study

We compared six deep learning architectures for pancreatic tumor localization in CT imaging. Our 3D Swin Transformer achieved the best performance (93.49% accuracy, 0.973 AUROC), validated through 5-fold cross-validation and statistical testing, and produced more accurate Grad-CAM++ explainability results than a 3D ViT baseline.