用知识蒸馏简化医学图像模型，让决策过程更透明可解释。
A Knowledge Distillation-Based Approach to Enhance Transparency of Classifier Models
- 用传统CNN做教师模型，通过蒸馏压缩为轻量学生模型。
- 减少网络层数后仍保持高准确率，且提升解释效率。
- 适合医疗AI领域需可解释性的研究与临床应用。
随着人工智能在医疗领域的快速发展，其可解释性需求日益增长。在医学图像分析中，高度透明和可解释的模型有助于临床医生更好地理解并信任AI的决策过程。本文提出一种基于知识蒸馏（Knowledge Distillation, KD）的方法，旨在提升医学图像分析中AI模型的透明度。首先使用传统卷积神经网络（CNN）构建教师模型，再通过知识蒸馏技术简化网络结构，减少层数，同时保留数据集的大部分特征。利用学生模型的特征图进行分层分析，识别关键特征与决策路径，生成直观的可视化解释。我们在三个公开医学数据集（脑肿瘤、眼病、阿尔茨海默病）上验证该方法，结果表明：即使层数显著减少，模型在测试集上仍表现优异，并大幅缩短了可解释性分析时间。
原文摘要 · Abstract (English)
With the rapid development of artificial intelligence (AI), especially in the medical field, the need for its explainability has grown. In medical image analysis, a high degree of transparency and model interpretability can help clinicians better understand and trust the decision-making process of AI models. In this study, we propose a Knowledge Distillation (KD)-based approach that aims to enhance the transparency of the AI model in medical image analysis. The initial step is to use traditional CNN to obtain a teacher model and then use KD to simplify the CNN architecture, retain most of the features of the data set, and reduce the number of network layers. It also uses the feature map of the student model to perform hierarchical analysis to identify key features and decision-making processes. This leads to intuitive visual explanations. We selected three public medical data sets (brain tumor, eye disease, and Alzheimer's disease) to test our method. It shows that even when the number of layers is reduced, our model provides a remarkable result in the test set and reduces the time required for the interpretability analysis.
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