论文标题
AI-MIA：COVID-19通过医学成像进行检测和严重性分析
AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging
论文作者
论文摘要
本文介绍了有组织的第二次Covid-19比赛的基线方法，该方法发生在欧洲计算机视觉会议（ECCV 2022）的Aimia研讨会框架内。它提出了COV19-CT-DB数据库，该数据库为COVID-19次数注释，由约7,700个3-D CT扫描组成。通过四个Covid-19严重性条件，进一步注释了由COVID-19病例组成的数据库的一部分。我们已经在培训，验证和测试数据集中将数据库和后半部分分开。前两个数据集用于培训和验证机器学习模型，而后者将用于评估开发模型。基线方法由基于CNN-RNN网络的深度学习方法组成，并在COVID19-CT-DB数据库上报告其性能。
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.
