Authors:
João Mari
1
;
Larissa Moreira
1
;
Leandro Silva
1
;
Mauricio Escarpinati
2
and
André Backes
3
Affiliations:
1
Institute of Exact and Technological Sciences, Federal University of Viçosa - UFV, Rio Paranaíba-MG, Brazil
;
2
School of Computer Science, Federal University of Uberlândia, Uberlândia-MG, Brazil
;
3
Department of Computing, Federal University of São Carlos, São Carlos-SP, Brazil
Keyword(s):
Breast Cancer, Deep Learning, Test-Time Augmentation, Image Classification.
Abstract:
Deep learning-based computer vision methods can improve diagnostic accuracy, efficiency, and productivity. While traditional approaches primarily apply Data Augmentation (DA) during the training phase, Test-Time Augmentation (TTA) offers a complementary strategy to improve the predictive capabilities of trained models without increasing training time. In this study, we propose a simple and effective TTA strategy to enhance the classification of histopathological images of breast cancer. After optimizing hyperparameters, we evaluated the TTA strategy across all magnifications of the BreakHis dataset using three deep learning architectures, trained with and without DA. We compared five sets of transformations and multiple prediction rounds. The proposed strategy significantly improved the mean accuracy across all magnifications, demonstrating its effectiveness in improving model performance.