Exploiting Test-Time Augmentation in Federated Learning for Brain Tumor MRI Classification
This work addresses the suboptimal classification performance in federated learning for brain tumor MRI images, which stems from lesion heterogeneity and image complexity. The authors propose a federated learning framework that integrates lightweight preprocessing with test-time augmentation (TTA). They present the first systematic validation of TTA’s effectiveness in federated medical image classification and demonstrate that combining TTA with lightweight preprocessing techniques—such as normalization and histogram equalization—yields significant and consistent improvements in classification accuracy (p<0.001). The proposed approach achieves reliable performance gains while maintaining computational efficiency, making it well-suited for resource-constrained, distributed healthcare settings.