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Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study.
Faes, Livia; Wagner, Siegfried K; Fu, Dun Jack; Liu, Xiaoxuan; Korot, Edward; Ledsam, Joseph R; Back, Trevor; Chopra, Reena; Pontikos, Nikolas; Kern, Christoph; Moraes, Gabriella; Schmid, Martin K; Sim, Dawn; Balaskas, Konstantinos; Bachmann, Lucas M; Denniston, Alastair K; Keane, Pearse A.
Affiliation
  • Faes L; Department of Ophthalmology, Cantonal Hospital Lucerne, Lucerne, Switzerland; Medical Retina Department, Moorfields Eye Hospital National Health Service Foundation Trust, London, UK.
  • Wagner SK; National Institute of Health Research Biomedical Research Center, Moorfields Eye Hospital National Health Service Foundation Trust, and University College London Institute of Ophthalmology, London, UK; Medical Retina Department, Moorfields Eye Hospital National Health Service Foundation Trust, Londo
  • Fu DJ; Medical Retina Department, Moorfields Eye Hospital National Health Service Foundation Trust, London, UK.
  • Liu X; National Institute of Health Research Biomedical Research Center, Moorfields Eye Hospital National Health Service Foundation Trust, and University College London Institute of Ophthalmology, London, UK; Department of Ophthalmology, University Hospitals Birmingham National Health Service Foundation Tr
  • Korot E; Medical Retina Department, Moorfields Eye Hospital National Health Service Foundation Trust, London, UK; Beaumont Eye Institute, Royal Oak, Michigan.
  • Ledsam JR; DeepMind, London, UK.
  • Back T; DeepMind, London, UK.
  • Chopra R; National Institute of Health Research Biomedical Research Center, Moorfields Eye Hospital National Health Service Foundation Trust, and University College London Institute of Ophthalmology, London, UK; Medical Retina Department, Moorfields Eye Hospital National Health Service Foundation Trust, Londo
  • Pontikos N; National Institute of Health Research Biomedical Research Center, Moorfields Eye Hospital National Health Service Foundation Trust, and University College London Institute of Ophthalmology, London, UK.
  • Kern C; Medical Retina Department, Moorfields Eye Hospital National Health Service Foundation Trust, London, UK; Department of Ophthalmology, University Hospital of Ludwig Maximilian University, Munich, Germany.
  • Moraes G; Medical Retina Department, Moorfields Eye Hospital National Health Service Foundation Trust, London, UK.
  • Schmid MK; Department of Ophthalmology, Cantonal Hospital Lucerne, Lucerne, Switzerland.
  • Sim D; National Institute of Health Research Biomedical Research Center, Moorfields Eye Hospital National Health Service Foundation Trust, and University College London Institute of Ophthalmology, London, UK; Medical Retina Department, Moorfields Eye Hospital National Health Service Foundation Trust, Londo
  • Balaskas K; National Institute of Health Research Biomedical Research Center, Moorfields Eye Hospital National Health Service Foundation Trust, and University College London Institute of Ophthalmology, London, UK; Medical Retina Department, Moorfields Eye Hospital National Health Service Foundation Trust, Londo
  • Bachmann LM; Medigntion, Zurich, Switzerland.
  • Denniston AK; National Institute of Health Research Biomedical Research Center, Moorfields Eye Hospital National Health Service Foundation Trust, and University College London Institute of Ophthalmology, London, UK; Department of Ophthalmology, University Hospitals Birmingham National Health Service Foundation Tr
  • Keane PA; National Institute of Health Research Biomedical Research Center, Moorfields Eye Hospital National Health Service Foundation Trust, and University College London Institute of Ophthalmology, London, UK; Medical Retina Department, Moorfields Eye Hospital National Health Service Foundation Trust, Londo
Lancet Digit Health ; 1(5): e232-e242, 2019 09.
Article in En | MEDLINE | ID: mdl-33323271
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Algorithms / Software / Data Interpretation, Statistical / Deep Learning Type of study: Diagnostic_studies / Prognostic_studies Aspects: Ethics / Implementation_research Limits: Adult / Humans Language: En Journal: Lancet Digit Health Year: 2019 Document type: Article Affiliation country: Reino Unido Country of publication: Reino Unido
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Algorithms / Software / Data Interpretation, Statistical / Deep Learning Type of study: Diagnostic_studies / Prognostic_studies Aspects: Ethics / Implementation_research Limits: Adult / Humans Language: En Journal: Lancet Digit Health Year: 2019 Document type: Article Affiliation country: Reino Unido Country of publication: Reino Unido