标题： 可解释性感知的高效医学图像分析剪枝 显示英文标题

标题： Interpretability-Aware Pruning for Efficient Medical Image Analysis

摘要： 深度学习在医学图像分析领域推动了重大进展，但其在临床实践中的应用仍受到现代模型规模庞大和缺乏透明度的限制。 可解释性技术的进展，如DL-Backtrace、逐层相关传播和综合梯度，使得评估在医学影像任务中训练的神经网络中各个组件的贡献成为可能。 在本工作中，我们引入了一个可解释性引导的剪枝框架，在保持预测性能和透明度的同时减少模型复杂度。 通过有选择地保留每一层中最相关部分，我们的方法实现了有针对性的压缩，同时保持具有临床意义的表示。 在多个医学图像分类基准上的实验表明，这种方法在准确率损失最小的情况下实现了高压缩率，为适用于医疗环境中实际部署的轻量级、可解释模型铺平了道路。 显示英文摘要

摘要： Deep learning has driven significant advances in medical image analysis, yet its adoption in clinical practice remains constrained by the large size and lack of transparency in modern models. Advances in interpretability techniques such as DL-Backtrace, Layer-wise Relevance Propagation, and Integrated Gradients make it possible to assess the contribution of individual components within neural networks trained on medical imaging tasks. In this work, we introduce an interpretability-guided pruning framework that reduces model complexity while preserving both predictive performance and transparency. By selectively retaining only the most relevant parts of each layer, our method enables targeted compression that maintains clinically meaningful representations. Experiments across multiple medical image classification benchmarks demonstrate that this approach achieves high compression rates with minimal loss in accuracy, paving the way for lightweight, interpretable models suited for real-world deployment in healthcare settings.