Kim, H.E.; Maros, M.E.; Miethke, T.; Kittel, M.; Siegel, F.; Ganslandt, T.
Lightweight Visual Transformers Outperform Convolutional Neural Networks for Gram-Stained Image Classification: An Empirical Study. Biomedicines 2023, 11, 1333.
https://doi.org/10.3390/biomedicines11051333
AMA Style
Kim HE, Maros ME, Miethke T, Kittel M, Siegel F, Ganslandt T.
Lightweight Visual Transformers Outperform Convolutional Neural Networks for Gram-Stained Image Classification: An Empirical Study. Biomedicines. 2023; 11(5):1333.
https://doi.org/10.3390/biomedicines11051333
Chicago/Turabian Style
Kim, Hee E., Mate E. Maros, Thomas Miethke, Maximilian Kittel, Fabian Siegel, and Thomas Ganslandt.
2023. "Lightweight Visual Transformers Outperform Convolutional Neural Networks for Gram-Stained Image Classification: An Empirical Study" Biomedicines 11, no. 5: 1333.
https://doi.org/10.3390/biomedicines11051333
APA Style
Kim, H. E., Maros, M. E., Miethke, T., Kittel, M., Siegel, F., & Ganslandt, T.
(2023). Lightweight Visual Transformers Outperform Convolutional Neural Networks for Gram-Stained Image Classification: An Empirical Study. Biomedicines, 11(5), 1333.
https://doi.org/10.3390/biomedicines11051333