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Updated: Nov 30, 2025

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Analyzing inter-reader variability affecting deep ensemble learning for COVID-19 detection in chest radiographs.

Sivaramakrishnan Rajaraman1, Sudhir Sornapudi2, Philip O Alderson3

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This summary is machine-generated.

This study enhances Coronavirus disease 2019 (COVID-19) detection in chest X-rays (CXRs) using deep learning ensembles. Ensemble models improved classification and localization, addressing challenges in medical image analysis.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Deep learning (DL) using convolutional neural networks (CNNs) excels in natural image tasks but faces challenges in medical imaging.
  • Limitations include adapting to medical image characteristics, modeling training noise, explaining black-box behavior, and handling ground truth variability.

Purpose of the Study:

  • To systematically address DL limitations for Coronavirus disease 2019 (COVID-19) detection in chest X-rays (CXRs).
  • To improve COVID-19 detection performance and clinical decision support through advanced DL techniques.

Main Methods:

  • Chest X-ray (CXR)-specific pretraining and fine-tuning of DL models.
  • Ensemble methods combining multiple fine-tuned models for improved performance.
  • Class-selective relevance mapping (CRM) for region of interest (ROI) localization and interpretation.
  • Simultaneous Truth and Performance Level Estimation (STAPLE) to analyze inter-reader variability and localization performance.

Main Results:

  • Ensemble approaches significantly enhanced both classification and localization performance for COVID-19 detection in CXRs.
  • Statistical analyses validated DL model performance across learning stages.
  • CRM effectively localized ROIs, aiding in the interpretation of individual and ensemble model behavior.
  • STAPLE analysis provided insights into inter-reader variability and guided algorithm optimization.

Conclusions:

  • Ensemble DL models offer a robust solution for COVID-19 detection in CXRs, overcoming limitations of single models.
  • Analyzing inter-reader variability and performance levels is crucial for guiding DL algorithm design and optimization in medical imaging.
  • This study pioneers ensemble-based disease ROI localization and performance analysis for COVID-19 CXR detection.