mlsanityData to 7 Oct 2026

POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)

arXiv 2004.12084 ↗First posted 25 Apr 2020eess.IV cs.CV cs.LGcited by 25

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Abstract (arXiv)

With the rapid development of COVID-19 into a global pandemic, there is an ever more urgent need for cheap, fast and reliable tools that can assist physicians in diagnosing COVID-19. Medical imaging such as CT can take a key role in complementing conventional diagnostic tools from molecular biology, and, using deep learning techniques, several automatic systems were demonstrated promising performances using CT or X-ray data. Here, we advocate a more prominent role of point-of-care ultrasound imaging to guide COVID-19 detection. Ultrasound is non-invasive and ubiquitous in medical facilities around the globe. Our contribution is threefold. First, we gather a lung ultrasound (POCUS) dataset consisting of 1103 images (654 COVID-19, 277 bacterial pneumonia and 172 healthy controls), sampled from 64 videos. This dataset was assembled from various online sources, processed specifically for deep learning models and is intended to serve as a starting point for an open-access initiative. Second, we train a deep convolutional neural network (POCOVID-Net) on this 3-class dataset and achieve an accuracy of 89% and, by a majority vote, a video accuracy of 92% . For detecting COVID-19 in particular, the model performs with a sensitivity of 0.96, a specificity of 0.79 and F1-score of 0.92 in a 5-fold cross validation. Third, we provide an open-access web service (POCOVIDScreen) that is available at: https://pocovidscreen.org. The website deploys the predictive model, allowing to perform predictions on ultrasound lung images. In addition, it grants medical staff the option to (bulk) upload their own screenings in order to contribute to the growing public database of pathological lung ultrasound images. Dataset and code are available from: https://github.com/jannisborn/covid19_pocus_ultrasound. NOTE: This preprint is superseded by our paper in Applied Sciences: https://doi.org/10.3390/app11020672

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Apr 2020.

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Citing papers per year: 2021: 10; 2022: 5; 2023: 4; 2024: 3; 2025: 3.

25 papers.

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  1. 2022Transformers in Medical Imaging: A Surveycited by 245
  2. 2021Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edgecited by 56
  3. 2021A Systematic Collection of Medical Image Datasets for Deep Learningcited by 29
  4. 2021COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analyticscited by 17
  5. 2023Large-scale Long-tailed Disease Diagnosis on Radiology Imagescited by 17
  6. 2024Large Language Model Benchmarks in Medical Taskscited by 16
  7. 2022Unsupervised Contrastive Learning of Image Representations from Ultrasound Videos with Hard Negative Miningcited by 14
  8. 2021POCFormer: A Lightweight Transformer Architecture for Detection of COVID-19 Using Point of Care Ultrasoundcited by 10
  9. 2023FPUS23: An Ultrasound Fetus Phantom Dataset with Deep Neural Network Evaluations for Fetus Orientations, Fetal Planes, and Anatomical Featurescited by 8
  10. 2022Dense Pixel-Labeling for Reverse-Transfer and Diagnostic Learning on Lung Ultrasound for COVID-19 and Pneumonia Detectioncited by 6
  11. 2025Ultrasound Lung Aeration Map via Physics-Aware Neural Operatorscited by 6
  12. 2021Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A reviewcited by 5
13 more
  1. 2022Weakly Supervised Contrastive Learning for Better Severity Scoring of Lung Ultrasoundcited by 4
  2. 2021COVID-Net US: A Tailored, Highly Efficient, Self-Attention Deep Convolutional Neural Network Design for Detection of COVID-19 Patient Cases from Point-of-care Ultrasound Imagingcited by 3
  3. 2021Adaptive Few-Shot Learning PoC Ultrasound COVID-19 Diagnostic Systemcited by 2
  4. 2022The Role of Pleura and Adipose in Lung Ultrasound AIcited by 2
  5. 2023A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Imagescited by 2
  6. 2023Self-Supervised Pretraining Improves Performance and Inference Efficiency in Multiple Lung Ultrasound Interpretation Taskscited by 2
  7. 2024Intra-video Positive Pairs in Self-Supervised Learning for Ultrasoundcited by 2
  8. 2025VidFuncta: Towards Generalizable Neural Representations for Ultrasound Videoscited by 2
  9. 2021Detection of COVID-19 Disease using Deep Neural Networks with Ultrasound Imagingcited by 1
  10. 2021Implementing a Detection System for COVID-19 based on Lung Ultrasound Imaging and Deep Learningcited by 1
  11. 2021Lung Ultrasound Segmentation and Adaptation between COVID-19 and Community-Acquired Pneumoniacited by 1
  12. 2025Auto-US: An Ultrasound Video Diagnosis Agent Using Video Classification Framework and LLMscited by 1
  13. 2024Ultrasound-Based AI for COVID-19 Detection: A Comprehensive Review of Public and Private Lung Ultrasound Datasets and Studiescited by 0