MIA-COV19D: COVID-19 Detection Through 3-D Chest CT Image Analysis

Dimitrios Kollias, Anastasios Arsenos, Levon Soukissian, Stefanos Kollias; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2021, pp. 537-544

Early and reliable COVID-19 diagnosis based on chest 3-D CT scans can assist medical specialists in vital circumstances. Deep learning methodologies constitute a main approach for chest CT scan analysis and disease prediction. However, large annotated databases are necessary for developing deep learning models that are able to provide COVID-19 diagnosis across various medical environments in different countries. Due to privacy issues, publicly available COVID-19 CT datasets are highly difficult to obtain, which hinders the research and development of AI-enabled diagnosis methods of COVID-19 based on CT scans. In this paper we present the COV19-CT-DB database which is annotated for COVID-19, consisting of about 5,000 3-D CT scans, We have split the database in training, validation and test datasets. The former two datasets can be used for training and validation of machine learning models, while the latter will be used for evaluation of the developed models. We present a deep learning approach, based on a CNN-RNN network and report its performance on the COVID19-CT-DB database. Moreover, we present the results of all main techniques that were developed and used in the ICCV COV19D Competition.