Lightweight Visual Transformers Outperform Convolutional Neural Networks for Gram-Stained Image Classification: An Empirical Study

We aimed to automate Gram-stain analysis to speed up the detection of bacterial strains in patients suffering from infections. We performed comparative analyses of visual transformers (VT) using various configurations including model size (small vs. large), training epochs (1 vs. 100), and quantizat...

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Detalles Bibliográficos
Autores principales: Kim, Hee E., Maros, Mate E., Miethke, Thomas, Kittel, Maximilian, Siegel, Fabian, Ganslandt, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10215960/
https://www.ncbi.nlm.nih.gov/pubmed/37239004
http://dx.doi.org/10.3390/biomedicines11051333

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10215960/
https://www.ncbi.nlm.nih.gov/pubmed/37239004
http://dx.doi.org/10.3390/biomedicines11051333

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