Speaker
Description
The progress of deep learning (DL) and artificial intelligence is astonishing, and it attracts numerous researchers and practitioners from multidisciplinary domains. Although tremendous literature regarding DL applications has been published across domains, it is uncommon that DL applications are actually deployed in the daily routine. This study empirically investigated popular deep learning models including transformers and convolutional neural networks in the medical domain in order to provide a gentle guideline to researchers and practitioners who consider utilizing deep learning models for a customized task.
References
Kim, Hee E., et al. "Lightweight Visual Transformers Outperform Convolutional Neural Networks for Gram-Stained Image Classification: An Empirical Study." Biomedicines 11.5 (2023): 1333.
| Keywords | Deep learning; vision transformer; convolutional neural network; image processing; transfer learning |
|---|
