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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
We are building an open database of COVID-19 cases with chest X-ray or CT images.
| Date | Stars |
|---|---|
| 2026-07-24 | 3061 |
| 2026-07-25 | 3061 |
| 2026-07-28 | 3062 |
| 2026-07-30 | 3062 |
| 2026-08-06 | 3062 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
#### 🛑 Note: please do not claim diagnostic performance of a model without a clinical study! This is not a kaggle competition dataset. Please read this paper about evaluation issues: [https://arxiv.org/abs/2004.12823](https://arxiv.org/abs/2004.12823) and [https://arxiv.org/abs/2004.05405](https://arxiv.org/abs/2004.05405)
## COVID-19 image data collection ([🎬 video about the project](https://www.youtube.com/watch?v=ineWmqfelEQ))
Project Summary: To build a public open dataset of chest X-ray and CT images of patients which are positive or suspected of COVID-19 or other viral and bacterial pneumonias ([MERS](https://en.wikipedia.org/wiki/Middle_East_respiratory_syndrome), [SARS](https://en.wikipedia.org/wiki/Severe_acute_respiratory_syndrome), and [ARDS](https://en.wikipedia.org/wiki/Acute_respiratory_distress_syndrome).). Data will be collected from public sources as well as through indirect collection from hospitals and physicians. All images and data will be released publicly in this GitHub repo.
This project is approved by the University of Montreal's Ethics Committee #CERSES-20-058-D
## View current [images](images) and [metadata](metadata.csv) and [a dataloader example](https://colab.research.google.com/drive/1A-gIZ6Xp-eh2b4CGS6BHH7-OgZtyjeP2)
The labels are arranged in a hierarchy:
<img width=300 src="docs/hierarchy.jpg"/>
Current stats of PA, AP, and AP Supine views. Labels 0=No or 1=Yes. Data loader is [here](https://github.com/mlmed/torchxrayvision/blob/master/torchxrayvision/datasets.py#L867)
```
COVID19_Dataset num_samples=481 views=['PA', 'AP']
{'ARDS': {0.0: 465, 1.0: 16},
'Bacterial': {0.0: 445, 1.0: 36},
'COVID-19': {0.0: 162, 1.0: 319},
'Chlamydophila': {0.0: 480, 1.0: 1},
'E.Coli': {0.0: 481},
'Fungal': {0.0: 459, 1.0: 22},
'Influenza': {0.0: 478, 1.0: 3},
'Klebsiella': {0.0: 474, 1.0: 7},
'Legionella': {0.0: 474, 1.0: 7},
'Lipoid': {0.0: 473, 1.0: 8},
'MERS': {0.0: 481},
'Mycoplasma': {0.0: 476, 1.0: 5},
'No Finding': {0.0: 467, 1.0: 14},
'Pneumocystis': {0.0: 459, 1.0: 22},
'Pneumonia': {0.0: 36, 1.0: 445},
'SARS': {0.0: 465, 1.0: 16},
'Streptococcus': {0.0: 467, 1.0: 14},
'Varicella': {0.0: 476, 1.0: 5},
'Viral': {0.0: 138, 1.0: 343}}
COVID19_Dataset num_samples=173 views=['AP Supine']
{'ARDS': {0.0: 170, 1.0: 3},
'Bacterial': {0.0: 169, 1.0: 4},
'COVID-19': {0.0: 41, 1.0: 132},
'Chlamydophila': {0.0: 173},
'E.Coli': {0.0: 169, 1.0: 4},
'Fungal': {0.0: 171, 1.0: 2},
'Influenza': {0.0: 173},
'Klebsiella': {0.0: 173},
'Legionella': {0.0: 173},
'Lipoid': {0.0: 173},
'MERS': {0.0: 173},
'Mycoplasma': {0.0: 173},
'No Finding': {0.0: 170, 1.0: 3},
'Pneumocystis': {0.0: 171, 1.0: 2},
'Pneumonia': {0.0: 26, 1.0: 147},
'SARS': {0.0: 173},
'Streptococcus': {0.0: 173},
'Varicella': {0.0: 173},
'Viral': {0.0: 41, 1.0: 132}}
```
## Annotations
[Lung Bounding Boxes](https://github.com/GeneralBlockchain/covid-19-chest-xray-lung-bounding-boxes-dataset) and [Chest X-ray Segmentation](https://github.com/GeneralBlockchain/covid-19-chest-xray-segmentations-dataset) (license: CC BY 4.0) contributed by [General Blockchain, Inc.](https://github.com/GeneralBlockchain)
[Pneumonia severity scores for 94 images](annotations/covid-severity-scores.csv) (license: CC BY-SA) from the paper [Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning](http://arxiv.org/abs/2005.11856)
[Generated Lung Segmentations](annotations/lungVAE-masks) (license: CC BY-SA) from the paper [Lung Segmentation from Chest X-rays using Variational Data Imputation](https://arxiv.org/abs/2005.10052)
[Brixia score for 192 images](https://github.com/BrixIA/Brixia-score-COVID-19) (license: CC BY-NC-SA) from the paper [End-to-end learning for semiquantitative rating of COVID-19 severity on Chest X-rays](https://arxiv.org/abs/2006.04603)
[Lung and other segmentations for 517 images](https://github.com/v7labs/covid-19-xray-dataset/tree/master/annotations) (license: CC BY) in COCO and raster formaExcerpt of 10,612 characters
Read on GitHub
Joseph Paul Cohen PhD
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Lan Dao · Canada
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Vishal Sharma
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Darío Hereñú
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:900e928b9910216a, topic:dataset, name:dataset, readme:dataset
matched fp:900e928b9910216a, topic:deep-learning
matched fp:900e928b9910216a, topic:computer-vision