The paper presents a comparative analysis of three distinct approaches based on deep learning for COVID-19 detection in chest CTs. The first approach is a volumetric one, involving 3D convolutions, while the other two approaches perform at first slice-wise classification and then aggregate the results at the volume level. The experiments are carried on the COV19-CT-DB dataset, with the aim of addressing the challenge raised by the MIA-COV19D Competition within ICCV 2021. Our best results on the validation subset reach a macro-F1 score of 0.92, which improves considerably the baseline score of 0.70 set by the organizers.

翻译：本文件根据对胸腔CT检测COVID-19的深入学习,对三种不同方法进行了比较分析,第一种是体积方法,涉及3D演化,而其他两种方法首先进行切片分类,然后在体积水平上汇总结果,先对COV19-CT-DB数据集进行实验,目的是应对在ICCV 2021年中MIA-COV19D竞争引起的挑战,我们在鉴定子集中取得的最佳结果达到0.92分的宏观-F1分,大大提高了组织者确定的0.70的基线分数。