Efficient brain tumor diagnosis is crucial for early treatment; however, it is challenging because of lesion variability and image complexity. We evaluated convolutional neural networks (CNNs) in a federated learning (FL) setting, comparing models trained on original versus preprocessed MRI images (resizing, grayscale conversion, normalization, filtering, and histogram equalization). Preprocessing alone yielded negligible gains; combined with test-time augmentation (TTA), it delivered consistent, statistically significant improvements in federated MRI classification (p<0.001). In practice, TTA should be the default inference strategy in FL-based medical imaging; when the computational budget permits, pairing TTA with light preprocessing provides additional reliable gains.
@article{arxiv.2601.12671,
title = {Exploiting Test-Time Augmentation in Federated Learning for Brain Tumor MRI Classification},
author = {Thamara Leandra de Deus Melo and Rodrigo Moreira and Larissa Ferreira Rodrigues Moreira and André Ricardo Backes},
journal= {arXiv preprint arXiv:2601.12671},
year = {2026}
}
Comments
21st International Conference on Computer Vision Theory and Applications (VISAPP 2026), 9-11 March 2026, Marbella, Spain