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

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: In: Biomedicines 11.5 (2023): 1333.

Event: Veröffentlichung

(where): Erlangen

(who): Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)

(when): 2023

Creator: Kim, Hee Eun
Maros, Máté E.
Miethke, Thomas
Kittel, Maximilian
Siegel, Fabian
Ganslandt, Thomas

DOI: 10.3390/biomedicines11051333

URN: urn:nbn:de:bvb:29-opus4-228316

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Last update: 14.08.2025, 10:58 AM CEST

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Kim, Hee Eun
Maros, Máté E.
Miethke, Thomas
Kittel, Maximilian
Siegel, Fabian
Ganslandt, Thomas
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)

Time of origin

2023

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

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