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Eight19 (United Kingdom)

United Kingdom

1 paper here1k citations across 1
fields
  • cs.LG1
ROR 03n0ge846OpenAlex

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most citedCommon pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

1k citations

researchers with a paper here
  • Evis Sala2 profiles2 · h 9
  • A. Ruggiero1 · h 0
  • Effrossyni Gkrania‐Klotsas1
  • G. Gozaliasl1 · h 0
  • G. Langs1 · h 3
  • G. Yang1 · h 0
  • J. Jacob1 · h 0
  • J. Lowe1 · h 0
  • J. Rudd1 · h 3
  • Julian Gilbey1
  • J. Weir-McCall1 · h 6
  • K. Bradley1 · h 1
collaborating institutions
  • Addenbrooke's HospitalGB1 paper
  • AstraZeneca (United Kingdom)GB1 paper
  • Boehringer Ingelheim (Germany)DE1 paper
  • Cambridge University Hospitals NHS Foundation TrustGB1 paper
  • Cancer Research UK Cambridge CenterGB1 paper
  • Chelsea and Westminster Hospital NHS Foundation TrustGB1 paper
  • Genomics (United Kingdom)GB1 paper
  • Huazhong University of Science and TechnologyCN1 paper
  • Imperial College LondonGB1 paper
  • Macau University of Science and TechnologyMO1 paper
  • Medical University of ViennaAT1 paper
  • Papworth HospitalGB1 paper

1 paper

cs.LG2020★ 1k cited

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Michael Roberts, Derek Driggs, Matthew Thorpe +13

Machine learning methods offer great promise for fast and accurate detection and prognostication of COVID-19 from standard-of-care chest radiographs (CXR) and computed tomography (…

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