A teacher-student knowledge distillation framework combining Swin Transformer and Vision Transformer reaches 99.78% and 99.28% accuracy with AUC 1.0 on two GI endoscopy datasets and uses Grad-CAM for interpretability.
Transfer learning based approach for lung and colon cancer detection using local binary pattern features and explainable artificial intelligence techniques
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A Graph-Augmented knowledge Distillation based Dual-Stream Vision Transformer with Region-Aware Attention for Gastrointestinal Disease Classification with Explainable AI
A teacher-student knowledge distillation framework combining Swin Transformer and Vision Transformer reaches 99.78% and 99.28% accuracy with AUC 1.0 on two GI endoscopy datasets and uses Grad-CAM for interpretability.