Acute Ischemic Stroke Onset Time Classification with Dynamic Convolution and Perfusion Maps Fusion

In treating acute ischemic stroke (AIS), determining the time since stroke onset (TSS) is crucial. Computed tomography perfusion (CTP) is vital for determining TSS by providing sufficient cerebral blood flow information. However, the CTP has small samples and high dimensions. In addition, the CTP is multi-map data, which has heterogeneity and complementarity. To address these issues, this paper demonstrates a classification model using CTP to classify the TSS of AIS patients. Firstly, we use dynamic convolution to improve model representation without increasing network complexity. Secondly, we use multi-scale feature fusion to fuse the local correlation of low-order features and use a transformer to fuse the global correlation of higher-order features. Finally, multi-head pooling attention is used to learn the feature information further and obtain as much important information as possible. We use a five-fold cross-validation strategy to verify the effectiveness of our method on the private dataset from a local hospital. The experimental results show that our proposed method achieves at least 5% higher accuracy than other methods in TTS classification task.