Artificial intelligence diagnosis and triage of large vessel occlusion may quicken clinical response for a subset of time-sensitive acute ischemic stroke patients, improving outcomes. Differences in architectural elements within data-driven convolutional neural network (CNN) models impact performance. Foreknowledge of effective model architectural elements for domain-specific problems can narrow the search for candidate models and inform strategic model design and adaptation to optimize performance on available data. Here, we study CNN architectures with a range of learnable parameters and which span the inclusion of architectural elements, such as parallel processing branches and residual connections with varying methods of recombining residual information.

中文翻译：

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卷积神经网络在计算机断层扫描血管造影中检测大血管闭塞的比较

大血管闭塞的人工智能诊断和分类可能会加快对时间敏感的急性缺血性中风患者子集的临床反应，从而改善预后。数据驱动的卷积神经网络 (CNN) 模型中架构元素的差异会影响性能。对针对特定领域问题的有效模型架构元素的预知可以缩小对候选模型的搜索范围，并为战略模型设计和调整提供信息，以优化可用数据的性能。在这里，我们研究了具有一系列可学习参数的 CNN 架构，它涵盖了架构元素的包含，例如并行处理分支和残差连接，以及各种重组残差信息的方法。