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Related Concept Videos

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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
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An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
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Related Experiment Video

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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COVID-19 detection based on self-supervised transfer learning using chest X-ray images.

Guang Li1, Ren Togo2, Takahiro Ogawa2

  • 1Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan.

International Journal of Computer Assisted Radiology and Surgery
|December 20, 2022
PubMed
Summary

This study introduces a novel self-supervised transfer learning method for detecting COVID-19 from chest X-rays. The approach significantly improves diagnostic accuracy and aids in reducing healthcare worker infections.

Keywords:
COVID-19 detectionChest X-ray imagesSelf-supervised learningTransfer learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer-Aided Diagnosis

Background:

  • The COVID-19 pandemic highlighted the need for rapid diagnostic tools.
  • Chest radiography (CXR) is crucial for identifying pneumonia, a common COVID-19 symptom.
  • Computer-aided detection (CAD) can enhance workflow efficiency and minimize infection risk for healthcare professionals.

Purpose of the Study:

  • To propose and evaluate a novel self-supervised transfer learning scheme for COVID-19 detection using CXR images.
  • To compare the proposed method against existing self-supervised learning (SSL) techniques and pre-trained deep convolutional neural networks (DCNNs).

Main Methods:

  • Developed a self-supervised transfer learning scheme for COVID-19 detection from CXR images.
  • Compared the proposed method with six SSL algorithms (Cross, BYOL, SimSiam, SimCLR, PIRL-jigsaw, PIRL-rotation).
  • Evaluated against six pre-trained DCNNs (ResNet18, ResNet50, ResNet101, CheXNet, DenseNet201, InceptionV3) on a large open COVID-19 CXR dataset.

Main Results:

  • The proposed method achieved a harmonic mean (HM) score of 0.985 and an Area Under the Curve (AUC) of 0.999.
  • Four-class accuracy reached 0.953.
  • Grad-CAM++ visualization enhanced the interpretability of the model's predictions.

Conclusions:

  • Transfer learning from natural images significantly benefits SSL for CXR analysis, boosting representation learning for COVID-19 detection.
  • The developed method shows promise in improving diagnostic accuracy and reducing infection risks for healthcare providers.