Abstract
In contemporary rural healthcare settings, the principal challenge in diagnosing brain images is the scarcity of available data, given that most of the existing deep learning models demand extensive training data to optimize their performance, necessitating centralized processing methods that potentially compromise data privacy. This paper proposes a novel framework tailored for brain tissue segmentation in rural healthcare facilities. The framework employs a deep reinforcement learning (DRL) environment in tandem with a refinement model (RM) deployed locally at rural healthcare sites. The proposed DRL model has a reduced parameter count and practicality for implementation across distributed rural sites. To uphold data privacy and enhance model generalization without transgressing privacy constraints, we employ federated learning (FL) for cooperative model training. We demonstrate the efficacy of our approach by training the network with a limited data set and observing a substantial performance enhancement, mitigating inaccuracies and irregularities in segmentation across diverse sites. Remarkably, the DRL model attains an accuracy of up to 80%, surpassing the capabilities of conventional convolutional neural networks when confronted with data insufficiency. Incorporating our RM results in an additional accuracy improvement of at least 10%, while FL contributes to a further accuracy enhancement of up to 5%. Collectively, the framework achieves an average 92% accuracy rate within rural healthcare settings characterized by data constraints.
Abstract (translated)
在当代农村医疗设施中，诊断脑图像的主要挑战是可用数据的稀缺性，因为大多数现有的深度学习模型需要大量训练数据来优化其性能，这导致需要集中处理方法，这可能危及数据隐私。本文提出了一种针对农村医疗设施脑组织分割的新型框架。该框架与在农村医疗设施局部部署的细化模型（RM）相结合。所提出的DRL模型具有减少的参数计数和实施上的实用性，可在分布式农村站点上实现。为了维护数据隐私并提高模型泛化能力，我们使用联邦学习（FL）进行合作模型训练。我们通过使用有限的数据集训练网络，并观察到显著的性能增强，从而减轻了站点间分割不准确和不规则的问题。值得注意的是，DRL模型在面临数据不足时，其准确率高达80%，超过了传统卷积神经网络的能力。将我们的RM结果并入至少10%的准确率提升，而FL则有助于实现5%的准确率提升。共同实现该框架在受数据约束的农村医疗设施中取得了92%的平均准确率。
URL
https://arxiv.org/abs/2403.00254
