让医学影像模型更小更透明，还能保持高精度。
Interpretability-Aware Pruning for Efficient Medical Image Analysis
- 用可解释性技术指导剪枝，保留关键神经元。
- 压缩率高，准确率损失小，多个数据集验证有效。
- 适合需要可信、轻量模型的临床部署场景。
深度学习推动了医学影像分析的进展，但其在临床应用中的推广仍受限于模型庞大且缺乏透明性。诸如DL-Backtrace、层间相关性传播（Layer-wise Relevance Propagation）和积分梯度（Integrated Gradients）等可解释性技术，使得评估神经网络中各组件对预测的贡献成为可能。本文提出一种基于可解释性的剪枝框架，在降低模型复杂度的同时，兼顾预测性能与透明性。通过有选择地保留每层中最相关的部分，该方法实现针对性压缩，同时维持具有临床意义的表征。在多个医学图像分类基准上的实验表明，该方法可在极小精度损失下实现高压缩率，为实际医疗环境中轻量化、可解释模型的部署铺平道路。
原文摘要 · Abstract (English)
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.
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