Interpretability-Aware Pruning for Efficient Medical Image Analysis
Journal:
arXiv
Published Date:
Jul 11, 2025
Abstract
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.
