论文标题
使用基于神经网络的方法与联合学习的基于神经网络的方法检测颅内出血检测
Intracranial Hemorrhage Detection Using Neural Network Based Methods With Federated Learning
论文作者
论文摘要
颅内出血发生，发生在颅骨内部，是一个严重的健康问题，需要快速且通常是强化的医疗。传统上，这种情况是由训练有素的专家分析患者的计算机断层扫描（CT）扫描，并确定出血的位置和类型，从而诊断出这种情况。我们提出了一种神经网络方法，以根据CT扫描查找和分类状态。该模型体系结构实现了时间分布式卷积网络。我们观察到从这种体系结构中的92％以上的准确性，提供了足够的数据。我们建议进一步扩展我们的方法，涉及联合学习的部署。这将有助于汇总学习的参数，而不会违反所涉及的数据的固有隐私。
Intracranial hemorrhage, bleeding that occurs inside the cranium, is a serious health problem requiring rapid and often intensive medical treatment. Such a condition is traditionally diagnosed by highly-trained specialists analyzing computed tomography (CT) scan of the patient and identifying the location and type of hemorrhage if one exists. We propose a neural network approach to find and classify the condition based upon the CT scan. The model architecture implements a time distributed convolutional network. We observed accuracy above 92% from such an architecture, provided enough data. We propose further extensions to our approach involving the deployment of federated learning. This would be helpful in pooling learned parameters without violating the inherent privacy of the data involved.
