在联邦学习中用测试时增强提升脑肿瘤影像分类效果
Exploiting Test-Time Augmentation in Federated Learning for Brain Tumor MRI Classification
- 将测试时增强与轻量预处理结合用于联邦学习
- 显著提升脑肿瘤分类准确率（p<0.001）
- 适合医疗影像联邦学习系统部署
高效脑肿瘤诊断对早期治疗至关重要，但受限于病灶多样性与图像复杂性。本文在联邦学习框架下评估卷积神经网络在原始与预处理MRI图像上的表现，预处理包括缩放、灰度化、归一化、滤波和直方图均衡化。仅使用预处理效果有限；而结合测试时增强（TTA）后，在联邦脑肿瘤分类任务中实现稳定且统计显著的性能提升（p<0.001）。实际应用中，应将TTA作为联邦学习医疗影像推理的默认策略；若计算资源允许，搭配轻量预处理可获得更可靠的性能增益。
原文摘要 · Abstract (English)
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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