中文标题#
可解釋的知識蒸餾用於高效的醫學影像分類
英文标题#
Explainable Knowledge Distillation for Efficient Medical Image Classification
中文摘要#
本研究全面探討了用於使用胸部 X 光（CXR）影像進行冠狀病毒病和肺癌分類的知識蒸餾框架。我們採用高容量教師模型，包括 VGG19 和輕量級視覺變換器（Visformer-S 和 AutoFormer-V2-T），以指導從 OFA-595 超網絡派生的緊湊型、硬體感知學生模型的訓練。我們的方法利用混合監督，結合真實標籤和教師模型的軟目標，以平衡準確性和計算效率。我們在兩個基準數據集上驗證了我們的模型：COVID-QU-Ex 和 LCS25000，涵蓋多個類別，包括冠狀病毒病、健康、非冠狀病毒肺炎、肺部和結腸癌。為了解釋模型的空間關注點，我們採用基於 Score-CAM 的可視化方法，這為教師和學生網絡的推理過程提供了見解。結果表明，蒸餾後的學生模型在顯著減少參數和推理時間的情況下保持了較高的分類性能，使其成為資源受限臨床環境中的最佳選擇。我們的工作強調了將模型效率與可解釋性相結合對於實際可信的醫療 AI 解決方案的重要性。
英文摘要#
This study comprehensively explores knowledge distillation frameworks for COVID-19 and lung cancer classification using chest X-ray (CXR) images. We employ high-capacity teacher models, including VGG19 and lightweight Vision Transformers (Visformer-S and AutoFormer-V2-T), to guide the training of a compact, hardware-aware student model derived from the OFA-595 supernet. Our approach leverages hybrid supervision, combining ground-truth labels with teacher models' soft targets to balance accuracy and computational efficiency. We validate our models on two benchmark datasets: COVID-QU-Ex and LCS25000, covering multiple classes, including COVID-19, healthy, non-COVID pneumonia, lung, and colon cancer. To interpret the spatial focus of the models, we employ Score-CAM-based visualizations, which provide insight into the reasoning process of both teacher and student networks. The results demonstrate that the distilled student model maintains high classification performance with significantly reduced parameters and inference time, making it an optimal choice in resource-constrained clinical environments. Our work underscores the importance of combining model efficiency with explainability for practical, trustworthy medical AI solutions.
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