# FDVTS's Solution for 2nd COV19D Competition on COVID-19 Detection and Severity Analysis

arXiv: https://arxiv.org/abs/2207.01758 · HTML: https://mlsanity.com/paper/2207.01758/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 5 Jul 2022.

## Builds on (most cited first, 6 of 6)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [ResNeSt: Split-Attention Networks](https://mlsanity.com/paper/2004.08955/) (2020)
- [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)
- [AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging](https://mlsanity.com/paper/2206.04732/) (2022)

## Built on by (most cited first, 4 of 4)

- [SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection](https://mlsanity.com/paper/2407.15728/) (2024)
- [COVID-19 Detection Using Slices Processing Techniques and a Modified Xception Classifier from Computed Tomography Images](https://mlsanity.com/paper/2312.07580/) (2023)
- [Enhanced detection of the presence and severity of COVID-19 from CT scans using lung segmentation](https://mlsanity.com/paper/2303.09440/) (2023)
- [MIA-3DCNN: COVID-19 Detection Based on a 3D CNN](https://mlsanity.com/paper/2303.10738/) (2023)

## Abstract (arXiv)

This paper presents our solution for the 2nd COVID-19 Competition, occurring in the framework of the AIMIA Workshop in the European Conference on Computer Vision (ECCV 2022). In our approach, we employ an effective 3D Contrastive Mixup Classification network for COVID-19 diagnosis on chest CT images, which is composed of contrastive representation learning and mixup classification. For the COVID-19 detection challenge, our approach reaches 0.9245 macro F1 score on 484 validation CT scans, which significantly outperforms the baseline method by 16.5%. In the COVID-19 severity detection challenge, our approach achieves 0.7186 macro F1 score on 61 validation samples, which also surpasses the baseline by 8.86%.