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

Kollias, Dimitrios, Arsenos, Anastasios and Kollias, Stefanos (2022) AI-MIA: COVID-19 Detection and Severity Analysis through Medical Imaging. In: ECCV 2022, 23-27/10/2022, Tel Aviv Israel.

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AI-MIA: COVID-19 Detection and Severity Analysis through Medical Imaging
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eccv22_aimia_comp_2cov19d_baseline (5).pdf - Whole Document
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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


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 detection,
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 is 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. The paper presents the results of both Challenges organised in the framework of the Competition, also compared to the performance of the baseline scheme.

Keywords:machine learning, medical imaging, COVID19 Diagnosis
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:College of Science > School of Computer Science
ID Code:50246
Deposited On:03 Oct 2022 10:55

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