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POLCOVID: a multicenter multiclass chest X-ray database (Poland, 2020–2021)

  • for the POLCOVID Study Group
  • Silesian University of Technology
  • Medical University of Silesia in Katowice
  • Voivodship Specialist Hospital
  • Central Clinical Hospital of the Ministry of Internal Affairs and Administration
  • Jagiellonian University Medical College
  • Kujawsko-Pomorskie Pulmonology Center
  • Silesian Hospital
  • Medical University of Warsaw
  • Nicolaus Copernicus University in Toruń
  • Medical University of Białystok
  • Wrocław Medical University
  • Czerniakowski Hospital
  • MEGREZ Hospital
  • Medical University of Gdańsk
  • Maria Sklodowska-Curie Institute of Oncology
  • Prognostic Specialist Clinic
  • District Hospital
  • University Clinical Hospital
  • University of Rzeszów
  • Yale University

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

The outbreak of the SARS-CoV-2 pandemic has put healthcare systems worldwide to their limits, resulting in increased waiting time for diagnosis and required medical assistance. With chest radiographs (CXR) being one of the most common COVID-19 diagnosis methods, many artificial intelligence tools for image-based COVID-19 detection have been developed, often trained on a small number of images from COVID-19-positive patients. Thus, the need for high-quality and well-annotated CXR image databases increased. This paper introduces POLCOVID dataset, containing chest X-ray (CXR) images of patients with COVID-19 or other-type pneumonia, and healthy individuals gathered from 15 Polish hospitals. The original radiographs are accompanied by the preprocessed images limited to the lung area and the corresponding lung masks obtained with the segmentation model. Moreover, the manually created lung masks are provided for a part of POLCOVID dataset and the other four publicly available CXR image collections. POLCOVID dataset can help in pneumonia or COVID-19 diagnosis, while the set of matched images and lung masks may serve for the development of lung segmentation solutions.

Original languageEnglish
Article number348
JournalScientific Data
Volume10
Issue number1
DOIs
Publication statusPublished - Dec 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

ASJC Scopus subject areas

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences

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