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Updated: Aug 16, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Bilateral adaptive graph convolutional network on CT based Covid-19 diagnosis with uncertainty-aware

Yanda Meng1, Joshua Bridge1, Cliff Addison2

  • 1Department of Eye and Vision Science, University of Liverpool, Liverpool, United Kingdom.

Medical Image Analysis
|December 27, 2022
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Summary

A new bilateral adaptive graph-based convolutional network (BA-GCN) model accurately diagnoses COVID-19 using chest CT scans. This uncertainty-aware consensus-assisted multiple instance learning approach enhances diagnostic accuracy and generalizability.

Keywords:
COVID-19CT imagesGraph convolutional networkMultiple instance learningUncertainty and consensus

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • The COVID-19 pandemic necessitates rapid and accurate diagnostic tools.
  • Chest computed tomography (CT) is crucial for early detection of COVID-19.
  • Existing methods may struggle with variability in CT scan slice numbers and feature extraction.

Purpose of the Study:

  • To develop an advanced model for reliable COVID-19 diagnosis from chest CT volumes.
  • To leverage uncertainty-aware consensus-assisted multiple instance learning (UC-MIL) for improved diagnostic reasoning.
  • To utilize both 2D and 3D discriminative information within CT volumes.

Main Methods:

  • Created the largest manual annotation dataset (7,768 slices) for lung segmentation in COVID-19 CT scans.
  • Trained a 2D segmentation model for lung masking and used UC-MIL to select reliable CT slices.
  • Developed a bilateral adaptive graph-based convolutional network (BA-GCN) integrating multi-level 2D and 3D features.

Main Results:

  • The BA-GCN model demonstrated reliable and accurate COVID-19 predictions across CT volumes with varying slice numbers.
  • The model outperformed existing approaches in learning and generalization abilities on three large COVID-19 CT datasets.
  • The UC-MIL approach effectively estimated prediction uncertainty and consensus for slice selection.

Conclusions:

  • The proposed BA-GCN model offers a robust solution for COVID-19 diagnosis using chest CT.
  • The integration of UC-MIL and graph convolutional networks enhances diagnostic performance and adaptability.
  • The study promotes reproducible research by releasing datasets and code.