earticle

영어

Data augmentation has been employed in neural networks for building robust models, not exclusively in the training phase but also in the testing stage, where the predictions of every transformed image are aggregated to a greater lustiness and upgraded accuracy. Furthermore, deep learning approaches applied in data augmentation, namely adversarial training, GANs, and Neural Style Transfer were applied while training the models, neither while testing them. In this work, we present a study of applying test-time Neural Style Transfer transformation in medical images as a method of augmentation in test time. Besides, we display the experiment's results of a classification task. Results reveal that the synthesized samples employed as modified images in the test time significantly improved the performance of the classification model.