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Explainable multi-instance and multi-task learning for COVID-19 diagnosis and lesion segmentation in CT images.

Minglei Li1, Xiang Li1, Yuchen Jiang1

  • 1Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China.

Knowledge-Based Systems
|July 5, 2022
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Summary

This study introduces a novel framework for simultaneous COVID-19 diagnosis and lung CT image segmentation using explainable AI. The method enhances accuracy and provides clinical explainability for better patient status analysis.

Keywords:
Adaptive multi-task learningAutomated diagnosisCOVID-19Explainable multi-instance learningLesion segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Coronavirus Disease 2019 (COVID-19) remains a global pandemic.
  • Chest CT scans are crucial for COVID-19 screening, offering detailed pathological information.
  • Current automated methods often treat diagnosis and segmentation as separate tasks, lacking clinical explainability.

Purpose of the Study:

  • To develop a unified, explainable multi-task learning framework for simultaneous COVID-19 diagnosis and multi-lesion segmentation in CT images.
  • To improve the accuracy and clinical interpretability of automated COVID-19 detection and analysis.
  • To enable severity assessment and lesion quantification for comprehensive patient status evaluation.

Main Methods:

  • Development of an explainable multi-instance multi-task network.
  • Simultaneous learning of task-related features with adaptive weights for diagnosis and segmentation.
  • Integration of lesion highlighting for explicable diagnostic results.

Main Results:

  • The proposed framework achieved superior performance in both COVID-19 diagnosis and multi-lesion segmentation compared to existing methods.
  • The model demonstrated enhanced accuracy in identifying and segmenting lesions within lung CT images.
  • The explainable nature of the network provided clinically relevant evidence for diagnoses.

Conclusions:

  • The developed multi-task learning framework effectively integrates COVID-19 diagnosis and CT image segmentation.
  • The explainable AI approach offers improved accuracy and interpretability for automated analysis of COVID-19.
  • This method holds promise for enhanced clinical decision-making in managing COVID-19 patients.