# COVID Detection in Chest CTs: Improving the Baseline on COV19-CT-DB

arXiv: https://arxiv.org/abs/2107.04808 · HTML: https://mlsanity.com/paper/2107.04808/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 15 references that are in the map.
- First posted on arXiv 10 Jul 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [When Does Label Smoothing Help?](https://mlsanity.com/paper/1906.02629/) (2019)
- [Deep Transparent Prediction through Latent Representation Analysis](https://mlsanity.com/paper/2009.07044/) (2020)
- [MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis](https://mlsanity.com/paper/2106.07524/) (2021)
- [sharpDARTS: Faster and More Accurate Differentiable Architecture Search](https://mlsanity.com/paper/1903.09900/) (2019)
- [Revealing Lung Affections from CTs. A Comparative Analysis of Various Deep Learning Approaches for Dealing with Volumetric Data](https://mlsanity.com/paper/2009.04160/) (2020)

## Built on by (most cited first, 3 of 3)

- [Diff3Dformer: Leveraging Slice Sequence Diffusion for Enhanced 3D CT Classification with Transformer Networks](https://mlsanity.com/paper/2406.17173/) (2024)
- [Res-Dense Net for 3D Covid Chest CT-scan classification](https://mlsanity.com/paper/2208.04613/) (2022)
- [Post-COVID Highlights: Challenges and Solutions of AI Techniques for Swift Identification of COVID-19](https://mlsanity.com/paper/2311.06258/) (2023)

## Abstract (arXiv)

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.