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Lightweight Visual Transformers Outperform Convolutional Neural Networks for Gram-Stained Image Classification: An Empirical Study.
Kim, Hee E; Maros, Mate E; Miethke, Thomas; Kittel, Maximilian; Siegel, Fabian; Ganslandt, Thomas.
Afiliação
  • Kim HE; Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health (CPD), Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany.
  • Maros ME; Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health (CPD), Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany.
  • Miethke T; Institute of Medical Microbiology and Hygiene, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany.
  • Kittel M; Institute for Clinical Chemistry, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany.
  • Siegel F; Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health (CPD), Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany.
  • Ganslandt T; Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany.
Biomedicines ; 11(5)2023 Apr 30.
Article em En | MEDLINE | ID: mdl-37239004
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies 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 Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article