论文标题
通过3D-Convnext和自定义预处理的Covid检测和严重性预测
COVID Detection and Severity Prediction with 3D-ConvNeXt and Custom Pretrainings
论文作者
论文摘要
由于Covid强烈影响呼吸系统，因此肺CT扫描可用于分析患者健康。我们引入了一个神经网络，用于预测肺损伤的严重程度，并使用三维CT-DATA检测共证。因此，我们将最新的Convnext模型调整为处理三维数据。此外，我们设计和分析了专门设计的不同训练方法，以提高模型处理三维CT-DATA的能力。我们在19 COVID19的严重性检测挑战中排名第二，在第二COVID19检测挑战中排名第三。
Since COVID strongly affects the respiratory system, lung CT-scans can be used for the analysis of a patients health. We introduce a neural network for the prediction of the severity of lung damage and the detection of a COVID-infection using three-dimensional CT-data. Therefore, we adapt the recent ConvNeXt model to process three-dimensional data. Furthermore, we design and analyze different pretraining methods specifically designed to improve the models ability to handle three-dimensional CT-data. We rank 2nd in the 1st COVID19 Severity Detection Challenge and 3rd in the 2nd COVID19 Detection Challenge.
