联邦学习可有效应对脑肿瘤分割中的影像强度异质性问题。
Validation of Various Normalization Methods for Brain Tumor Segmentation: Can Federated Learning Overcome This Heterogeneity?
- 用不同归一化方法制造数据异质性，模拟真实医疗数据分布差异。
- 联邦学习在异构数据下仍保持92%的3D Dice分数，接近集中式训练效果。
- 适合关注医疗隐私保护与模型泛化能力的研究者参考。
深度学习在医学影像中应用日益广泛，但需大量数据，面临数据隐私、存储与传输等挑战。联邦学习（FL）可缓解这些问题，但在非独立同分布（non-IID）数据下性能可能下降。本研究通过在不同数据子集上应用多种MRI强度归一化方法，模拟非IID条件，评估其对脑肿瘤分割模型训练与推理的影响。结果表明，联邦学习对客户端间不一致的归一化数据具有鲁棒性，在测试中达到92%的3D Dice分数，与集中式训练模型表现相当。这证明联邦学习可在不违反数据隐私的前提下，有效训练高性能分割模型，对医疗应用具有重要意义。代码已公开于：https://github.com/SanoScience/fl-varying-normalization。
原文摘要 · Abstract (English)
Deep learning (DL) has been increasingly applied in medical imaging, however, it requires large amounts of data, which raises many challenges related to data privacy, storage, and transfer. Federated learning (FL) is a training paradigm that overcomes these issues, though its effectiveness may be reduced when dealing with non-independent and identically distributed (non-IID) data. This study simulates non-IID conditions by applying different MRI intensity normalization techniques to separate data subsets, reflecting a common cause of heterogeneity. These subsets are then used for training and testing models for brain tumor segmentation. The findings provide insights into the influence of the MRI intensity normalization methods on segmentation models, both training and inference. Notably, the FL methods demonstrated resilience to inconsistently normalized data across clients, achieving the 3D Dice score of 92%, which is comparable to a centralized model (trained using all data). These results indicate that FL is a solution to effectively train high-performing models without violating data privacy, a crucial concern in medical applications. The code is available at: https://github.com/SanoScience/fl-varying-normalization.
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