PaperlayerView exact revision on arXiv

Source-linked AI summary

COVID-19 Image Data Collection

Joseph Paul Cohen, Paul Morrison, Lan Dao

arXiv:2003.11597v1eess.IVcs.CVcs.LGq-bio.QM

TL;DR

The paper addresses the lack of a public COVID-19 chest-imaging collection designed for computational analysis. It assembles a public database from websites, publications, and extracted PDF images, with the intended use of developing tools for diagnosis, pneumonia characterization, progression monitoring, and outcome prediction.

  • Problem

    Public datasets contained more typical chest X-rays, but no collection of COVID-19 chest X-rays or CT scans was designed for computational analysis.

    Source: §1 · p. 1

  • Method

    The authors assemble a public database of COVID-19 and other pneumonia cases using images and metadata collected from public websites, publications, and PDF extractions.

    Source: §1 · p. 1, §3 · p. 2

  • Results

    The resulting dataset is intended to support tools for identifying COVID-19 characteristics, distinguishing pneumonia types, monitoring progression, and predicting outcomes.

    Source: §1 · p. 1, §2 · pp. 1–2

  • Takeaways & Limitations

    The collection provides data for studying COVID-19 radiological findings and developing computational approaches to diagnosis and patient management.

    Source: §2 · p. 1

Abstract

from arXiv · show

This paper describes the initial COVID-19 open image data collection. It was created by assembling medical images from websites and publications and currently contains 123 frontal view X-rays.

1. Motivation

The paper addresses the lack of computationally usable COVID-19 chest-imaging data by introducing a public database assembled from public sources. The authors envision using it to develop tools for identifying COVID-19 characteristics and comparing pneumonia types.

Motivation

  • There was no public collection of COVID-19 chest X-rays or CT scans designed for computational analysis.Large public datasets existed for more typical chest X-rays, but not for COVID-19 imaging.Source: §1 · p. 1

Contribution

  • The paper introduces a public database containing COVID-19, MERS, SARS, and ARDS pneumonia cases with chest X-ray or CT images.The images and data were collected from public sources to avoid infringing patient confidentiality.Source: §1 · p. 1

Motivation

  • The database is intended to support deep-learning systems that identify COVID-19 characteristics, distinguish pneumonia types, or predict survival.The authors specifically mention transfer learning as a likely approach.Source: §1 · p. 1

Release

  • The images and data were released through a public GitHub URL after being collected from already-public sources.Source: §1 · p. 1

2. Expected outcome

The dataset is intended to support research on COVID-19 progression, radiological differences, diagnosis, triage, and patient outcomes. Proposed tools could also help monitor disease evolution and inform treatment understanding.

Scientific uses

  • The dataset can support study of COVID-19 progression and variation in radiological findings from other pneumonias.Source: §2 · p. 1

Prediction and management

  • Tools could predict pneumonia type and outcome, including survival, to facilitate patient management.The text frames these capabilities as potential future developments.Source: §2 · p. 1

Triage

  • Tools could triage cases when physical tests are unavailable, particularly during shortages of polymerase chain reaction tests.Source: §2 · p. 1

Monitoring

  • Monitoring COVID-19-positive patients could help track disease evolution and improve understanding of disease dynamics and treatments.Source: §2 · p. 2

3. Dataset

The dataset was assembled from websites, publications, and extracted PDF images while preserving image quality and collecting metadata attributes. Its source material includes a set of cited medical papers.

Data collection

  • Images and metadata were compiled from websites including Radiopaedia.org, the Italian Society of Medical and Interventional Radiology, and Figure1.com.Images were also extracted from online publications, websites, or PDFs using pdfimages.Source: §3 · p. 2
  • The collection process aimed to maintain image quality during extraction.Source: §3 · p. 2

Sources

  • The dataset drew medical images from the cited papers listed in the dataset section.Source: §3 · p. 2

Metadata

  • Metadata attributes are described in a dedicated table accompanying the dataset.Source: Table 2 · p. 2

Related papers

Loading 2003.11597v1…