A Knowledge Distillation-Based Approach to Enhance Transparency of Classifier Models

TL;DR

This paper introduces a knowledge distillation method to improve the transparency and interpretability of CNN-based medical image analysis models, making them more understandable for clinicians.

Contribution

It proposes a KD-based approach that simplifies CNN architectures while retaining key features, enhancing model interpretability with visual explanations.

Findings

Effective in reducing model complexity

Maintains high accuracy on medical datasets

Speeds up interpretability analysis

Abstract

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.