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Updated: Jul 13, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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How Does Pruning Impact Long-Tailed Multi-label Medical Image Classifiers?

Gregory Holste¹, Ziyu Jiang², Ajay Jaiswal¹

¹The University of Texas at Austin, Austin, TX, USA.

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|October 13, 2023

Summary

This summary is machine-generated.

Neural network pruning for chest X-ray diagnosis affects rare diseases more. Radiologists found pruned models identified challenging cases with more noise and lower image quality.

Keywords:

Chest X-Ray Imbalance Long-Tailed Learning Pruning

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Deep neural network pruning reduces model size and inference time.
Pruning's impact on models for long-tailed, multi-label medical data is not well understood.
This knowledge gap poses risks for clinical diagnostic applications.

Purpose of the Study:

To analyze the effects of pruning on neural networks diagnosing thorax diseases from chest X-rays (CXRs).
To investigate how pruning impacts different diseases based on frequency and co-occurrence.
To identify and evaluate challenging cases where pruned and unpruned models disagree.

Main Methods:

Analysis of pruning effects on two large CXR datasets.
Characterization of class "forgettability" in pruned models.
Identification of pruning-identified exemplars (PIEs) and a human reader study with radiologists.

Main Results:

Pruning disproportionately affects the diagnosis of less frequent diseases.
Radiologists perceived PIEs as having more label noise, lower image quality, and higher diagnostic difficulty.
Class forgettability correlates with disease frequency and co-occurrence patterns.

Conclusions:

Pruning deep neural networks for CXR diagnosis requires careful consideration of its impact on rare diseases.
Understanding model disagreements (PIEs) is crucial for safe deployment.
This study provides foundational insights into pruning effects in clinical AI.