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Imaging Studies for Cardiovascular System III: X-Ray01:20

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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.
Definition and Purpose
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

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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Weakly Labeled Data Augmentation for Deep Learning: A Study on COVID-19 Detection in Chest X-Rays.

Sivaramakrishnan Rajaraman1, Sameer Antani1

  • 1Lister Hill National Center for Biomedical Communications, National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA.

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PubMed
Summary

This study enhances artificial intelligence (AI) for detecting COVID-19 pneumonia on chest X-rays (CXRs) by using weakly labeled data augmentation. Results show AI models trained with augmented data significantly improve COVID-19 detection accuracy compared to non-augmented data.

Keywords:
COVID-19augmentationchest X-raysconvolutional neural networkdeep learninglocalizationpneumonia

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Diseases

Background:

  • The COVID-19 pandemic has led to a surge in pneumonia cases requiring radiological diagnosis.
  • Chest X-rays (CXRs) are crucial for diagnosing COVID-19 pneumonia but place a burden on radiology services.
  • Developing AI tools for CXR analysis is hindered by a lack of sufficient training data.

Purpose of the Study:

  • To improve AI-based detection of COVID-19 pneumonia on CXRs.
  • To investigate the efficacy of weakly labeled data augmentation for training AI models.
  • To compare the performance of AI models trained with augmented versus non-augmented data.

Main Methods:

  • Utilized weakly labeled CXR images from public pneumonia datasets to augment training data.
  • Employed a stage-wise approach to train convolutional neural network (CNN) algorithms.
  • Compared AI model performance using augmented data against baseline non-augmented training.

Main Results:

  • Weakly labeled data augmentation significantly improved AI model accuracy in identifying viral pneumonia compared to non-augmented training (Acc: 0.5555/0.6536 vs. 0.2885/0.5028).
  • Adding COVID-19 CXRs to augmented training data further boosted performance (Acc: 0.7095/0.8889).
  • Findings suggest COVID-19 presents uniquely on CXRs compared to other viral pneumonias.

Conclusions:

  • Weakly labeled data augmentation is an effective strategy to enhance AI performance for COVID-19 pneumonia detection on CXRs.
  • AI models trained with augmented data show superior accuracy in identifying COVID-19 manifestations.
  • COVID-19 pneumonia exhibits distinct radiological features on CXRs compared to other viral pneumonias.