Interpretability-Aware Pruning for Efficient Medical Image Analysis

TL;DR

This paper presents an interpretability-guided pruning method that reduces the size of medical image analysis models while maintaining accuracy and transparency, facilitating deployment in clinical environments.

Contribution

It introduces a novel pruning framework that leverages interpretability techniques to selectively compress neural networks without sacrificing interpretability or performance.

Findings

Achieves high compression rates with minimal accuracy loss

Maintains interpretability of pruned models

Demonstrates effectiveness across multiple medical imaging benchmarks

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.