AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging

• URL: http://arxiv.org/abs/2206.04732v2
• Date: Mon, 13 Jun 2022 09:03:34 GMT
• Title: AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging
• Authors: Dimitrios Kollias and Anastasios Arsenos and Stefanos Kollias
• Abstract summary: The COV19-CT-DB database is annotated for COVID-19 detction, consisting of about 7,700 3-D CT scans. We have split the database and the latter part of it in training, validation and test datasets. 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.
• Score: 3.6170587429082195
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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