论文标题
竞争性的深度神经网络方法，用于ImagecledMed Stafion 2020任务
A Competitive Deep Neural Network Approach for the ImageCLEFmed Caption 2020 Task
论文作者
论文摘要
想象中的字幕任务的目的是开发一个系统，该系统自动用相关的医学概念标记放射学图像。我们描述了基于深度神经网络（DNN）解决此问题的方法。在3,534次放射学图像的挑战测试集上，我们的系统达到0.375的F1得分，排名高，在所有成功提交挑战的系统中排名第12，我们仅依靠提供的数据源，并且不使用任何外部医学知识或任何外部医学知识或其他医学图像库库或应用程序模型或应用程序模型。
The aim of ImageCLEFmed Caption task is to develop a system that automatically labels radiology images with relevant medical concepts. We describe our Deep Neural Network (DNN) based approach for tackling this problem. On the challenge test set of 3,534 radiology images, our system achieves an F1 score of 0.375 and ranks high, 12th among all systems that were successfully submitted to the challenge, whereby we only rely on the provided data sources and do not use any external medical knowledge or ontologies, or pretrained models from other medical image repositories or application domains.
