Your browser doesn't support javascript.
loading
A deep-learning algorithm to classify skin lesions from mpox virus infection.
Thieme, Alexander H; Zheng, Yuanning; Machiraju, Gautam; Sadee, Chris; Mittermaier, Mirja; Gertler, Maximilian; Salinas, Jorge L; Srinivasan, Krithika; Gyawali, Prashnna; Carrillo-Perez, Francisco; Capodici, Angelo; Uhlig, Maximilian; Habenicht, Daniel; Löser, Anastassia; Kohler, Maja; Schuessler, Maximilian; Kaul, David; Gollrad, Johannes; Ma, Jackie; Lippert, Christoph; Billick, Kendall; Bogoch, Isaac; Hernandez-Boussard, Tina; Geldsetzer, Pascal; Gevaert, Olivier.
Afiliação
  • Thieme AH; Department of Medicine, Stanford University, Stanford, CA, USA. thieme@stanford.edu.
  • Zheng Y; Stanford Center for Biomedical Informatics Research (BMIR), Department of Biomedical Data Science, Stanford University, Stanford, USA. thieme@stanford.edu.
  • Machiraju G; Department of Radiation Oncology, Charité-Universitätsmedizin Berlin, Berlin, Germany. thieme@stanford.edu.
  • Sadee C; Berlin Institute of Health at Charité-Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charité Digital Clinician Scientist Program, Berlin, Berlin, Germany. thieme@stanford.edu.
  • Mittermaier M; Department of Medicine, Stanford University, Stanford, CA, USA.
  • Gertler M; Stanford Center for Biomedical Informatics Research (BMIR), Department of Biomedical Data Science, Stanford University, Stanford, USA.
  • Salinas JL; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
  • Srinivasan K; Department of Medicine, Stanford University, Stanford, CA, USA.
  • Gyawali P; Stanford Center for Biomedical Informatics Research (BMIR), Department of Biomedical Data Science, Stanford University, Stanford, USA.
  • Carrillo-Perez F; Berlin Institute of Health at Charité-Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charité Digital Clinician Scientist Program, Berlin, Berlin, Germany.
  • Capodici A; Department of Infectious Diseases and Respiratory Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany.
  • Uhlig M; Institute of Tropical Medicine and International Health, Charité-Universitätsmedizin Berlin, Berlin, Germany.
  • Habenicht D; Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, CA, USA.
  • Löser A; Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, CA, USA.
  • Kohler M; Department of Medicine, Stanford University, Stanford, CA, USA.
  • Schuessler M; Department of Medicine, Stanford University, Stanford, CA, USA.
  • Kaul D; Stanford Center for Biomedical Informatics Research (BMIR), Department of Biomedical Data Science, Stanford University, Stanford, USA.
  • Gollrad J; Department of Architecture and Computer Technology (ATC), University of Granada, Granada, Spain.
  • Ma J; Department of Medicine, Stanford University, Stanford, CA, USA.
  • Lippert C; Stanford Center for Biomedical Informatics Research (BMIR), Department of Biomedical Data Science, Stanford University, Stanford, USA.
  • Billick K; Department of Biomedical and Neuromotor Science, Alma Mater Studiorum-University of Bologna, Bologna, Italy.
  • Bogoch I; Department of Medicine, Justus-Liebig-Universität Gießen, Gießen, Germany.
  • Hernandez-Boussard T; Technical University Berlin, Berlin, Germany.
  • Geldsetzer P; Department of Radiotherapy, University Medical Center Schleswig-Holstein, Lübeck, Germany.
  • Gevaert O; Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg, Germany.
Nat Med ; 29(3): 738-747, 2023 03.
Article em En | MEDLINE | ID: mdl-36864252
Assuntos
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Mpox / Aprendizado Profundo Tipo de estudo: Observational_studies / Prognostic_studies Limite: Humans / Male Idioma: En Ano de publicação: 2023 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Mpox / Aprendizado Profundo Tipo de estudo: Observational_studies / Prognostic_studies Limite: Humans / Male Idioma: En Ano de publicação: 2023 Tipo de documento: Article