mlsanityData to 7 Oct 2026

Boosting COVID-19 Severity Detection with Infection-aware Contrastive Mixup Classification

arXiv 2211.14559 ↗First posted 26 Nov 2022eess.IV cs.CVcited by 7

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Abstract (arXiv)

This paper presents our solution for the 2nd COVID-19 Severity Detection Competition. This task aims to distinguish the Mild, Moderate, Severe, and Critical grades in COVID-19 chest CT images. In our approach, we devise a novel infection-aware 3D Contrastive Mixup Classification network for severity grading. Specifcally, we train two segmentation networks to first extract the lung region and then the inner lesion region. The lesion segmentation mask serves as complementary information for the original CT slices. To relieve the issue of imbalanced data distribution, we further improve the advanced Contrastive Mixup Classification network by weighted cross-entropy loss. On the COVID-19 severity detection leaderboard, our approach won the first place with a Macro F1 Score of 51.76%. It significantly outperforms the baseline method by over 11.46%.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 22 of its 42 references that are in the map. First posted on arXiv 26 Nov 2022.

Builds on

22 of 42 references.

References · 22

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2016Densely Connected Convolutional Networkscited by 6,691
  4. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  5. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  6. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
  7. 2020Supervised Contrastive Learningcited by 2,803
  8. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  9. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  10. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  11. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,345
  12. 2020ResNeSt: Split-Attention Networkscited by 445
10 more
  1. 2020Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Imagescited by 184
  2. 2020Deep Transparent Prediction through Latent Representation Analysiscited by 72
  3. 2021MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysiscited by 72
  4. 2020Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19cited by 62
  5. 2020Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentationcited by 53
  6. 2020Lung Infection Quantification of COVID-19 in CT Images with Deep Learningcited by 28
  7. 2022AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imagingcited by 20
  8. 2020Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Imagescited by 13
  9. 2020Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Imagescited by 8
  10. 2020CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Imagecited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 2; 2026: 2.

7 papers.

  1. 2024Advancing COVID-19 Detection in 3D CT Scanscited by 5
  2. 2025Multi-Source COVID-19 Detection via Variance Risk Extrapolationcited by 5
  3. 2025Advancing Lung Disease Diagnosis in 3D CT Scanscited by 4
  4. 2024Domain Adaptation Using Pseudo Labels for COVID-19 Detectioncited by 3
  5. 20232D and 3D CNN-Based Fusion Approach for COVID-19 Severity Prediction from 3D CT-Scanscited by 0
  6. 2026Clinical Priors Guided Lung Disease Detection in 3D CT Scanscited by 0
  7. 2026Vision-Language Model Based Multi-Expert Fusion for CT Image Classificationcited by 0