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%。

基于云的联邦学习框架用于 MRI 分割

Cloud-based Federated Learning Framework for MRI Segmentation

Brain tissue segmentation is essential for neuroscience and clinical studies.
However, segmentation on longitudinal data is challenging due to dynamic brain
changes across the lifespan. Previous researches mainly focus on
self-supervision with regularizations and will lose longitudinal generalization
when fine-tuning on a specific age group. In this paper, we propose a dual
meta-learning paradigm to learn longitudinally consistent representations and
persist when fine-tuning. Specifically, we learn a plug-and-play feature
extractor to extract longitudinal-consistent anatomical representations by
meta-feature learning and a well-initialized task head for fine-tuning by
meta-initialization learning. Besides, two class-aware regularizations are
proposed to encourage longitudinal consistency. Experimental results on the
iSeg2019 and ADNI datasets demonstrate the effectiveness of our method. Our
code is available at this https URL

本文提出了一种双元元学习范式，通过元特征学习提取纵向一致的解剖学特征表示，并通过元初始化学习提供良好初始化的任务头进行微调。此外，提出了两种类感知的正则化方法，以鼓励纵向一致性。在 iSeg2019 和 ADNI 数据集上的实验结果证明了我们方法的有效性。