This paper presents the baseline approach for the organized 2nd Covid-19 Competition, occurring in the framework of the AIMIA Workshop in the European Conference on Computer Vision (ECCV 2022). It presents the COV19-CT-DB database which is annotated for COVID-19 detction, consisting of about 7,700 3-D CT scans. Part of the database consisting of Covid-19 cases is further annotated in terms of four Covid-19 severity conditions. We have split the database and the latter part of it in training, validation and test datasets. The former two datasets are used for training and validation of machine learning models, while the latter will be used for evaluation of the developed models. The baseline approach consists of a deep learning approach, based on a CNN-RNN network and report its performance on the COVID19-CT-DB database.
翻译:本文介绍了在欧洲计算机远景会议(ECCV 2022)的AIMIA讲习班框架内举行的有组织的第二次Covid-19竞争的基线方法,介绍了COV19-CT-DB数据库,该数据库附有COVID-19分解说明,由大约7,700个3D-CT扫描组成,由Covid-19案件组成的部分数据库以四种Covid-19严重程度条件进一步附加说明,我们将数据库和后一部分在培训、验证和测试数据集中分开使用,前两套数据集用于机器学习模型的培训和验证,而后者将用于对已开发模型的评价,基线方法包括以CNN-RNNN网络为基础的深入学习方法,并在COVID19-CT-DB数据库上报告其业绩。