CNN-CASS：CNN用于MPR图像中冠状动脉狭窄评分的分类

为了减少患者诊断冠状动脉疾病的等待时间，应用自动方法使用冠状动脉计算机断层扫描血管造影术扫描或提取的多平面重建（MPR）图像来确定其严重程度，从而使医生可以根据每个病例的优先级考虑第二点。先前研究的主要缺点是缺乏能够保证其可靠性的大量数据。..

CNN-CASS: CNN for Classification of Coronary Artery Stenosis Score in MPR Images

To decrease patient waiting time for diagnosis of the Coronary Artery Disease, automatic methods are applied to identify its severity using Coronary Computed Tomography Angiography scans or extracted Multiplanar Reconstruction (MPR) images, giving doctors a second-opinion on the priority of each case. The main disadvantage of previous studies is the lack of large set of data that could guarantee their reliability.Another limitation is the usage of handcrafted features requiring manual preprocessing, such as centerline extraction. We overcome both limitations by applying a different automated approach based on ShuffleNet V2 network architecture and testing it on the proposed collected dataset of MPR images, which is bigger than any other used in this field before. We also omit centerline extraction step and train and test our model using whole curved MPR images of 708 and 105 patients, respectively. The model predicts one of three classes: 'no stenosis' for normal, 'non-significant' - 1-50% of stenosis detected, 'significant' - more than 50% of stenosis. We demonstrate model's interpretability through visualization of the most important features selected by the network. For stenosis score classification, the method shows improved performance comparing to previous works, achieving 80% accuracy on the patient level. Our code is publicly available.