Explainable dual-teacher knowledge distillation with confidence-aware knowledge filtering for lightweight wound segmentation

Abstract

Medical image segmentation plays a crucial role in automated wound assessment; however, achieving high accuracy while maintaining computational efficiency and interpretability remains a significant challenge. Even though knowledge distillation can improve overall efficiency in most cases, relying entirely on the teacher(s) can lead to poor predictions. This thesis presents a novel framework that integrates explainable artificial intelligence with an efficient knowledge distillation pipeline for wound segmentation. A dual-teacher knowledge distillation strategy is employed to transfer complementary representations from heterogeneous high-capacity models to a lightweight student network. To enhance the reliability of knowledge transfer, a confidence-aware knowledge filtering mechanism is introduced, which selectively guides the student model using only high-confidence predictions from the teachers. In addition, a lightweight yet effective student architecture, termed SE-HybridConv- Tiny-UNet, is proposed. This model incorporates hybrid convolutional operations and channel attention mechanisms to improve feature representation while maintaining a low parameter count. Extensive experiments conducted on wound segmentation datasets demonstrate that the proposed framework achieves competitive performance compared to larger models, while significantly reducing computational complexity. Furthermore, explainability is incorporated using GradCAM++, enabling visual validation of model decisions and ensuring that predictions are based on clinically relevant regions. The consistency observed between validation and test-time explanations highlights the generalization capability and reliability of the proposed approach. Overall, this work establishes an effective balance between accuracy, efficiency, and interpretability, making it suitable for deployment in real-world, resource-constrained healthcare environments.