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CLARE-XR: explainable regression-based classification of chest radiographs with label embeddings.

Joana Rocha1,2, Sofia Cardoso Pereira3,4, Pedro Sousa5

  • 1Institute for Systems and Computer Engineering Technology and Science (INESC-TEC), Porto, 4200-465, Portugal. joana.m.rocha@inesctec.pt.

Scientific Reports
|December 28, 2024
PubMed
Summary

CLARE-XR is a novel system for chest X-ray pathology classification. It provides interpretable explanations by referencing similar cases, enhancing trust and decision-making in medical imaging analysis.

Keywords:
Data-centricDeep learningExplainabilityMedical imageMulti-labelX-ray

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Radiology

Background:

  • Accurate pathology classification in chest X-rays is crucial for clinical decision-making.
  • Existing systems often lack transparency, hindering end-user trust and regulatory compliance.
  • Interpretability is essential for the adoption of AI in medical diagnostics.

Purpose of the Study:

  • To introduce CLARE-XR, a novel methodology for interpretable chest X-ray pathology classification.
  • To provide visual explanations for diagnoses by retrieving similar cases.
  • To enhance end-user trust and support clinical decision-making.

Main Methods:

  • Developed a regression model to map chest X-ray images into a 2D latent space.
  • Generated reference coordinates for pathologies via label embedding of ground-truth annotations.
  • Inferred classification based on the distance to reference coordinates and retrieved similar training images.

Main Results:

  • CLARE-XR demonstrated superior performance compared to a ResNet50 baseline on the NIH ChestX-ray14 dataset.
  • The system successfully identified pathologies and provided explanations through case retrieval.
  • The methodology offers inherent interpretability and discloses classification rules.

Conclusions:

  • CLARE-XR offers an interpretable framework for chest X-ray analysis, moving beyond predictive performance.
  • The system mimics clinical practice by comparing current scans with similar past cases.
  • This approach enhances trust and aids regulatory compliance in AI-driven medical diagnostics.