mlsanityData to 7 Oct 2026

COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images

arXiv 2003.09871 ↗First posted 22 Mar 2020eess.IV cs.CV cs.LGcited by 264

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Cited by 26 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The COVID-19 pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is effective screening of infected patients, with one of the key screening approaches being radiology examination using chest radiography. Motivated by this and inspired by the open source efforts of the research community, in this study we introduce COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public. To the best of the authors' knowledge, COVID-Net is one of the first open source network designs for COVID-19 detection from CXR images at the time of initial release. We also introduce COVIDx, an open access benchmark dataset that we generated comprising of 13,975 CXR images across 13,870 patient patient cases, with the largest number of publicly available COVID-19 positive cases to the best of the authors' knowledge. Furthermore, we investigate how COVID-Net makes predictions using an explainability method in an attempt to not only gain deeper insights into critical factors associated with COVID cases, which can aid clinicians in improved screening, but also audit COVID-Net in a responsible and transparent manner to validate that it is making decisions based on relevant information from the CXR images. By no means a production-ready solution, the hope is that the open access COVID-Net, along with the description on constructing the open source COVIDx dataset, will be leveraged and build upon by both researchers and citizen data scientists alike to accelerate the development of highly accurate yet practical deep learning solutions for detecting COVID-19 cases and accelerate treatment of those who need it the most.

Key facts. Cited by 264 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Mar 2020.

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Citing papers per year: 2021: 100; 2022: 48; 2023: 44; 2024: 42; 2025: 21; 2026: 9.

264 papers.

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  1. 2022Transformers in Medical Imaging: A Surveycited by 245
  2. 2024OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLMcited by 182
  3. 2022Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learningcited by 159
  4. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
  5. 2021Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyondcited by 75
  6. 2022Full-Spectrum Out-of-Distribution Detectioncited by 63
  7. 2023Large AI Models in Health Informatics: Applications, Challenges, and the Futurecited by 61
  8. 2023LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matchingcited by 60
  9. 2023Med-UniC: Unifying Cross-Lingual Medical Vision-Language Pre-Training by Diminishing Biascited by 57
  10. 2021Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edgecited by 56
  11. 2022OSegNet: Operational Segmentation Network for COVID-19 Detection using Chest X-ray Imagescited by 47
  12. 2021COVID-19 Infection Localization and Severity Grading from Chest X-ray Imagescited by 46
38 more
  1. 2021FLOP: Federated Learning on Medical Datasets using Partial Networkscited by 42
  2. 2022VeriFi: Towards Verifiable Federated Unlearningcited by 41
  3. 2021Deep Learning-based Patient Re-identification Is able to Exploit the Biometric Nature of Medical Chest X-ray Datacited by 38
  4. 2022Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectivescited by 32
  5. 2022Explainable Deep Learning Methods in Medical Image Classification: A Surveycited by 31
  6. 2021All you need are a few pixels: semantic segmentation with PixelPickcited by 30
  7. 2021Machine learning based disease diagnosis: A comprehensive reviewcited by 30
  8. 2021COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse Learningcited by 29
  9. 2021A Systematic Collection of Medical Image Datasets for Deep Learningcited by 29
  10. 2021VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labelscited by 27
  11. 2022Federated Learning with GAN-based Data Synthesis for Non-IID Clientscited by 25
  12. 2023Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good Teachercited by 25
  13. 2023Medical Vision Language Pretraining: A surveycited by 24
  14. 2024The Clever Hans Effect in Unsupervised Learningcited by 24
  15. 2021Self-supervised Pseudo Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical Imagescited by 23
  16. 2023IMITATE: Clinical Prior Guided Hierarchical Vision-Language Pre-trainingcited by 23
  17. 2024MLIP: Enhancing Medical Visual Representation with Divergence Encoder and Knowledge-guided Contrastive Learningcited by 23
  18. 2021Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Imagescited by 22
  19. 2021FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumoniacited by 22
  20. 2021COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-ray Imagescited by 22
  21. 2022Vision Transformers in Medical Computer Vision -- A Contemplative Retrospectioncited by 22
  22. 2024EVA-X: A Foundation Model for General Chest X-ray Analysis with Self-supervised Learningcited by 22
  23. 2021Vision Transformer using Low-level Chest X-ray Feature Corpus for COVID-19 Diagnosis and Severity Quantificationcited by 21
  24. 2021Label-Assemble: Leveraging Multiple Datasets with Partial Labelscited by 20
  25. 2022RadioTransformer: A Cascaded Global-Focal Transformer for Visual Attention-guided Disease Classificationcited by 19
  26. 2021Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Datacited by 18
  27. 2024Anatomical Structure-Guided Medical Vision-Language Pre-trainingcited by 18
  28. 2021COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analyticscited by 17
  29. 2024MedViLaM: A multimodal large language model with advanced generalizability and explainability for medical data understanding and generationcited by 17
  30. 2021Self-supervised deep convolutional neural network for chest X-ray classificationcited by 16
  31. 2023HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagationcited by 16
  32. 2023Utilizing Synthetic Data for Medical Vision-Language Pre-training: Bypassing the Need for Real Imagescited by 16
  33. 2021Parametric Scattering Networkscited by 15
  34. 2021Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution Viewcited by 15
  35. 2022Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencodercited by 15
  36. 2024A comprehensive study on fidelity metrics for XAIcited by 14
  37. 2024A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysiscited by 14
  38. 2022Image Data collection and implementation of deep learning-based model in detecting Monkeypox disease using modified VGG16cited by 13

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