COVID-19 presence classification and severity prediction via (3D) thorax computed tomography scans have become important tasks in recent times. Especially for capacity planning of intensive care units, predicting the future severity of a COVID-19 patient is crucial. The presented approach follows state-of-theart techniques to aid medical professionals in these situations. It comprises an ensemble learning strategy via 5-fold cross-validation that includes transfer learning and combines pre-trained 3D-versions of ResNet34 and DenseNet121 for COVID19 classification and severity prediction respectively. Further, domain-specific preprocessing was applied to optimize model performance. In addition, medical information like the infection-lung-ratio, patient age, and sex were included. The presented model achieves an AUC of 79.0% to predict COVID-19 severity, and 83.7% AUC to classify the presence of an infection, which is comparable with other currently popular methods. This approach is implemented using the AUCMEDI framework and relies on well-known network architectures to ensure robustness and reproducibility.

翻译：基于（三维）胸部CT扫描进行COVID-19存在性分类与严重程度预测已成为近年来的重要任务。特别是在重症监护病房容量规划中，预测COVID-19患者未来病情严重程度至关重要。本文提出的方法遵循最新技术，旨在为医护人员提供辅助支持。该方法采用基于5折交叉验证的集成学习策略，融合迁移学习技术，分别利用预训练的ResNet34和DenseNet121三维版本进行COVID-19分类与严重程度预测。此外，通过领域特异性预处理优化模型性能，并纳入感染-肺比、患者年龄及性别等医学信息。该模型在预测COVID-19严重程度时达到79.0%的AUC，在感染存在性分类中达到83.7%的AUC，性能与当前主流方法相当。该方案基于AUCMEDI框架实现，并采用广为人知的网络架构以确保鲁棒性与可复现性。