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.

本研究提出了一种面向农村医疗机构的脑组织分割新框架，采用深度强化学习环境与本地部署的细化模型相结合。该框架通过有限数据集的训练，验证了其在多样性场所的分割准确性，使得平均准确率达到 92%。