论文标题
COV3D：使用3D Resnets从CT扫描中检测COVID-19的存在和严重程度
Cov3d: Detection of the presence and severity of COVID-19 from CT scans using 3D ResNets
论文作者
论文摘要
深度学习已被用来协助分析医学成像。一种用途是在受试者中检测到COVID-19的计算机断层扫描（CT）扫描的分类。本文介绍了COV3D，这是一个三维卷积神经网络，用于检测胸部CT扫描中COVID19的存在和严重程度。在具有人类专家注释的COV19-CT-DB数据集中，它在验证设置中以检测COVID19的存在的验证设置，达到了0.9476的宏F1分数。对于对CoVID19的严重性进行分类的任务，它的宏F1得分为0.7552。两种结果都在2022年的“支持AI支持的医学图像分析研讨会和Covid-19诊断竞争”（MIA-COV19D）的基线结果改善。
Deep learning has been used to assist in the analysis of medical imaging. One such use is the classification of Computed Tomography (CT) scans when detecting for COVID-19 in subjects. This paper presents Cov3d, a three dimensional convolutional neural network for detecting the presence and severity of COVID19 from chest CT scans. Trained on the COV19-CT-DB dataset with human expert annotations, it achieves a macro f1 score of 0.9476 on the validation set for the task of detecting the presence of COVID19. For the task of classifying the severity of COVID19, it achieves a macro f1 score of 0.7552. Both results improve on the baseline results of the `AI-enabled Medical Image Analysis Workshop and Covid-19 Diagnosis Competition' (MIA-COV19D) in 2022.
