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Label correlation guided discriminative label feature learning for multi-label chest image classification.

Kai Zhang1, Wei Liang1, Peng Cao2

  • 1Computer Science and Engineering, Northeastern University, Shenyang, China.

Computer Methods and Programs in Biomedicine
|January 20, 2024
PubMed
Summary

This study introduces a novel multi-label learning framework to improve Chest X-ray (CXR) classification by effectively learning and utilizing intricate label correlations. The proposed method significantly enhances classification performance, outperforming existing state-of-the-art approaches.

Keywords:
Consistency constraintLabel correlationsMulti-label chest X-rayMulti-label supervised contrastive loss

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare

Background:

  • Multi-label Chest X-ray (CXR) classification benefits from understanding label relationships.
  • Existing methods struggle to effectively capture and leverage these complex label correlations.

Purpose of the Study:

  • To propose a novel multi-label learning framework for CXR image classification.
  • To effectively learn and utilize intricate label correlations for improved performance.

Main Methods:

  • Global label correlations are captured using a self-attention mechanism.
  • Image-level features are decomposed into label-level features to guide feature learning.
  • End-to-end enhancement of label-level features via consistency constraints and a guided contrastive loss.

Main Results:

  • The proposed approach achieved an average F1 score of 44.6% and an AUC of 76.5% on the CheXpert dataset.
  • Demonstrated a 7.7% F1 score and 1.3% AUC improvement over state-of-the-art methods.
  • Validated through three times 5-fold cross-validation.

Conclusions:

  • Accurate learning and utilization of label correlations lead to more discriminative label-level features.
  • The proposed framework achieves highly competitive performance in multi-label CXR classification.