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

2026-01-19

AI Summary

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.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Computer Vision: Medical and Biological Imaging
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

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.

Problem

• federated learning
• brain tumor
• MRI classification
• test-time augmentation
• medical imaging

Innovation

• Test-Time Augmentation
• Federated Learning
• Brain Tumor Classification
• MRI Preprocessing
• Medical Image Analysis