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arXiv · 2601.12671

Exploiting Test-Time Augmentation in Federated Learning for Brain Tumor MRI Classification

Thamara Leandra de Deus Melo·Rodrigo Moreira·Larissa Ferreira Rodrigues Moreira·André Ricardo Backes
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Abstract

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.

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Thamara Leandra de Deus Melo, Rodrigo Moreira, Larissa Ferreira Rodrigues Moreira, André Ricardo Backes. 2026-01-19. Exploiting Test-Time Augmentation in Federated Learning for Brain Tumor MRI Classification. https://arxiv.org/abs/2601.12671

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