COVID Detection in Chest CTs: Improving the Baseline on COV19-CT-DB
This work addresses the problem of COVID-19 diagnosis for medical imaging practitioners, but it is incremental as it builds on existing methods for a specific competition dataset.
The paper tackled COVID-19 detection in chest CT scans by comparing three deep learning approaches, achieving a macro-F1 score of 0.92 on the validation subset, which significantly improved the baseline of 0.70.
The paper presents a comparative analysis of three distinct approaches based on deep learning for COVID-19 detection in chest CTs. The first approach is a volumetric one, involving 3D convolutions, while the other two approaches perform at first slice-wise classification and then aggregate the results at the volume level. The experiments are carried on the COV19-CT-DB dataset, with the aim of addressing the challenge raised by the MIA-COV19D Competition within ICCV 2021. Our best results on the validation subset reach a macro-F1 score of 0.92, which improves considerably the baseline score of 0.70 set by the organizers.