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.
翻译:脑膜内出血,是严重的健康问题,需要迅速和经常密集的治疗,这种病症传统上是由训练有素的专家诊断的,他们分析病人的计算断层扫描,如果有的话,则确定出出出血的地点和类型。我们建议采用神经网络方法,根据CT扫描找到病症并进行分类。模型结构采用一个时间分布的连锁网络。我们从这种结构中观察到了92%以上的准确性,提供了足够的数据。我们提议进一步扩展我们采用联结学习的方法,这将有助于在不侵犯所涉数据固有隐私的情况下汇集所学参数。