Exploiting Test-Time Augmentation in Federated Learning for Brain Tumor MRI Classification
This work addresses efficient brain tumor diagnosis for medical imaging applications, but it is incremental as it applies existing TTA techniques to a federated learning context.
The paper tackled brain tumor MRI classification in federated learning by evaluating CNNs with preprocessing and test-time augmentation, finding that TTA alone provided statistically significant improvements (p<0.001), and combining it with light preprocessing offered additional gains when computationally feasible.
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.