Abstract
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: this https URL.
Abstract (translated)
深度学习（DL）在医学影像领域得到了越来越多的应用，然而它需要大量的数据，这带来了与数据隐私、存储和传输相关的许多挑战。联邦学习（FL）是一种训练范式，可以克服这些问题，尽管当处理非独立同分布（non-IID）的数据时，其效果可能会降低。本研究通过在不同的MRI强度标准化技术应用于分离出的数据子集上来模拟非IID条件，这反映了异质性的一个常见原因。这些子集随后被用于训练和测试脑肿瘤分割模型。研究结果提供了关于MRI强度标准化方法对分割模型（包括训练和推断）影响的见解。值得注意的是，联邦学习方法展示了对客户端之间不一致标准化数据的鲁棒性，在3D Dice分数上达到了92%，这与集中式模型（使用所有数据进行训练）的结果相当。这些结果表明，联邦学习可以有效训练高性能模型而不侵犯数据隐私，这是医学应用中的一个关键问题。代码可在以下网址获得：[此处提供实际链接]。
URL
https://arxiv.org/abs/2510.07126
