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--- a/README.md +++ b/README.md @@ -1,168 +1,167 @@ - -#### 🛑 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 formats by [v7labs](https://github.com/v7labs/covid-19-xray-dataset) - -## Contribute - - - Submit data directly to the project. View our [research protocol](https://docs.google.com/document/d/14b7cou98YhYcJ2jwOKznChtn5y6-mi9bgjeFv2DxOt0/edit). Contact us to start the process. - - We can extract images from publications. Help identify publications which are not already included using a GitHub issue (DOIs we have are listed in the metadata file). There is a searchable database of COVID-19 papers [here](https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov), and a non-searchable one (requires download) [here](https://pages.semanticscholar.org/coronavirus-research). - - - Submit data to these sites (we can scrape the data from them): - - https://radiopaedia.org/ (license CC BY-NC-SA) - - https://www.sirm.org/category/senza-categoria/covid-19/ - - https://www.eurorad.org/ (license CC BY-NC-SA) - - https://coronacases.org/ (preferred for CT scans, license Apache 2.0) - - - Provide bounding box/masks for the detection of problematic regions in images already collected. - - - See [SCHEMA.md](SCHEMA.md) for more information on the metadata schema. - -*Formats:* For chest X-ray dcm, jpg, or png are preferred. For CT nifti (in gzip format) is preferred but also dcms. Please contact with any questions. - -## Background - -In the context of a COVID-19 pandemic, we want to improve prognostic predictions to triage and manage patient care. Data is the first step to developing any diagnostic/prognostic tool. While there exist large public datasets of more typical chest X-rays from the NIH [Wang 2017], Spain [Bustos 2019], Stanford [Irvin 2019], MIT [Johnson 2019] and Indiana University [Demner-Fushman 2016], there is no collection of COVID-19 chest X-rays or CT scans designed to be used for computational analysis. - -The 2019 novel coronavirus (COVID-19) presents several unique features [Fang, 2020](https://pubs.rsna.org/doi/10.1148/radiol.2020200432) and [Ai 2020](https://pubs.rsna.org/doi/10.1148/radiol.2020200642). While the diagnosis is confirmed using polymerase chain reaction (PCR), infected patients with pneumonia may present on chest X-ray and computed tomography (CT) images with a pattern that is only moderately characteristic for the human eye [Ng, 2020](https://pubs.rsna.org/doi/10.1148/ryct.2020200034). In late January, a Chinese team published a paper detailing the clinical and paraclinical features of COVID-19. They reported that patients present abnormalities in chest CT images with most having bilateral involvement [Huang 2020](https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30183-5/fulltext). Bilateral multiple lobular and subsegmental areas of consolidation constitute the typical findings in chest CT images of intensive care unit (ICU) patients on admission [Huang 2020](https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30183-5/fulltext). In comparison, non-ICU patients show bilateral ground-glass opacity and subsegmental areas of consolidation in their chest CT images [Huang 2020](https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30183-5/fulltext). In these patients, later chest CT images display bilateral ground-glass opacity with resolved consolidation [Huang 2020](https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30183-5/fulltext). - - -## Goal - -Our goal is to use these images to develop AI based approaches to predict and understand the infection. Our group will work to release these models using our open source [Chester AI Radiology Assistant platform](https://mlmed.org/tools/xray/). - -The tasks are as follows using chest X-ray or CT (preference for X-ray) as input to predict these tasks: - -- Healthy vs Pneumonia (prototype already implemented [Chester](https://mlmed.org/tools/xray/) with ~74% AUC, validation study [here](https://arxiv.org/abs/2002.02497)) - -- ~~Bacterial vs Viral vs COVID-19 Pneumonia~~ (not relevant enough for the clinical workflows) - -- Prognostic/severity predictions (survival, need for intubation, need for supplemental oxygen) - -## Expected outcomes - -Tool impact: This would give physicians an edge and allow them to act with more confidence while they wait for the analysis of a radiologist by having a digital second opinion confirm their assessment of a patient's condition. Also, these tools can provide quantitative scores to consider and use in studies. - -Data impact: Image data linked with clinically relevant attributes in a public dataset that is designed for ML will enable parallel development of these tools and rapid local validation of models. Furthermore, this data can be used for completely different tasks. - - -## Contact -PI: [Joseph Paul Cohen. Postdoctoral Fellow, Mila, University of Montreal](https://josephpcohen.com/) - -## Citations - -Second Paper available [here](http://arxiv.org/abs/2006.11988) and [source code for baselines](https://github.com/mlmed/torchxrayvision/tree/master/scripts/covid-baselines) - -``` -COVID-19 Image Data Collection: Prospective Predictions Are the Future -Joseph Paul Cohen and Paul Morrison and Lan Dao and Karsten Roth and Tim Q Duong and Marzyeh Ghassemi -arXiv:2006.11988, https://github.com/ieee8023/covid-chestxray-dataset, 2020 -``` - -``` -@article{cohen2020covidProspective, - title={COVID-19 Image Data Collection: Prospective Predictions Are the Future}, - author={Joseph Paul Cohen and Paul Morrison and Lan Dao and Karsten Roth and Tim Q Duong and Marzyeh Ghassemi}, - journal={arXiv 2006.11988}, - url={https://github.com/ieee8023/covid-chestxray-dataset}, - year={2020} -} -``` - -Paper available [here](https://arxiv.org/abs/2003.11597) - -``` -COVID-19 image data collection, arXiv:2003.11597, 2020 -Joseph Paul Cohen and Paul Morrison and Lan Dao -https://github.com/ieee8023/covid-chestxray-dataset -``` - -``` -@article{cohen2020covid, - title={COVID-19 image data collection}, - author={Joseph Paul Cohen and Paul Morrison and Lan Dao}, - journal={arXiv 2003.11597}, - url={https://github.com/ieee8023/covid-chestxray-dataset}, - year={2020} -} -``` - -<meta name="citation_title" content="COVID-19 image data collection" /> -<meta name="citation_publication_date" content="2020" /> -<meta name="citation_author" content="Joseph Paul Cohen and Paul Morrison and Lan Dao" /> - -## License - -Each image has license specified in the metadata.csv file. Including Apache 2.0, CC BY-NC-SA 4.0, CC BY 4.0. - -The metadata.csv, scripts, and other documents are released under a CC BY-NC-SA 4.0 license. Companies are free to perform research. Beyond that contact us. +#### 🛑 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="https://github.com/ieee8023/covid-chestxray-dataset/blob/master/docs/hierarchy.jpg?raw=true"/> + + +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 formats by [v7labs](https://github.com/v7labs/covid-19-xray-dataset) + +## Contribute + + - Submit data directly to the project. View our [research protocol](https://docs.google.com/document/d/14b7cou98YhYcJ2jwOKznChtn5y6-mi9bgjeFv2DxOt0/edit). Contact us to start the process. + - We can extract images from publications. Help identify publications which are not already included using a GitHub issue (DOIs we have are listed in the metadata file). There is a searchable database of COVID-19 papers [here](https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov), and a non-searchable one (requires download) [here](https://pages.semanticscholar.org/coronavirus-research). + + - Submit data to these sites (we can scrape the data from them): + - https://radiopaedia.org/ (license CC BY-NC-SA) + - https://www.sirm.org/category/senza-categoria/covid-19/ + - https://www.eurorad.org/ (license CC BY-NC-SA) + - https://coronacases.org/ (preferred for CT scans, license Apache 2.0) + + - Provide bounding box/masks for the detection of problematic regions in images already collected. + + - See [SCHEMA.md](SCHEMA.md) for more information on the metadata schema. + +*Formats:* For chest X-ray dcm, jpg, or png are preferred. For CT nifti (in gzip format) is preferred but also dcms. Please contact with any questions. + +## Background + +In the context of a COVID-19 pandemic, we want to improve prognostic predictions to triage and manage patient care. Data is the first step to developing any diagnostic/prognostic tool. While there exist large public datasets of more typical chest X-rays from the NIH [Wang 2017], Spain [Bustos 2019], Stanford [Irvin 2019], MIT [Johnson 2019] and Indiana University [Demner-Fushman 2016], there is no collection of COVID-19 chest X-rays or CT scans designed to be used for computational analysis. + +The 2019 novel coronavirus (COVID-19) presents several unique features [Fang, 2020](https://pubs.rsna.org/doi/10.1148/radiol.2020200432) and [Ai 2020](https://pubs.rsna.org/doi/10.1148/radiol.2020200642). While the diagnosis is confirmed using polymerase chain reaction (PCR), infected patients with pneumonia may present on chest X-ray and computed tomography (CT) images with a pattern that is only moderately characteristic for the human eye [Ng, 2020](https://pubs.rsna.org/doi/10.1148/ryct.2020200034). In late January, a Chinese team published a paper detailing the clinical and paraclinical features of COVID-19. They reported that patients present abnormalities in chest CT images with most having bilateral involvement [Huang 2020](https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30183-5/fulltext). Bilateral multiple lobular and subsegmental areas of consolidation constitute the typical findings in chest CT images of intensive care unit (ICU) patients on admission [Huang 2020](https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30183-5/fulltext). In comparison, non-ICU patients show bilateral ground-glass opacity and subsegmental areas of consolidation in their chest CT images [Huang 2020](https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30183-5/fulltext). In these patients, later chest CT images display bilateral ground-glass opacity with resolved consolidation [Huang 2020](https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30183-5/fulltext). + + +## Goal + +Our goal is to use these images to develop AI based approaches to predict and understand the infection. Our group will work to release these models using our open source [Chester AI Radiology Assistant platform](https://mlmed.org/tools/xray/). + +The tasks are as follows using chest X-ray or CT (preference for X-ray) as input to predict these tasks: + +- Healthy vs Pneumonia (prototype already implemented [Chester](https://mlmed.org/tools/xray/) with ~74% AUC, validation study [here](https://arxiv.org/abs/2002.02497)) + +- ~~Bacterial vs Viral vs COVID-19 Pneumonia~~ (not relevant enough for the clinical workflows) + +- Prognostic/severity predictions (survival, need for intubation, need for supplemental oxygen) + +## Expected outcomes + +Tool impact: This would give physicians an edge and allow them to act with more confidence while they wait for the analysis of a radiologist by having a digital second opinion confirm their assessment of a patient's condition. Also, these tools can provide quantitative scores to consider and use in studies. + +Data impact: Image data linked with clinically relevant attributes in a public dataset that is designed for ML will enable parallel development of these tools and rapid local validation of models. Furthermore, this data can be used for completely different tasks. + + +## Contact +PI: [Joseph Paul Cohen. Postdoctoral Fellow, Mila, University of Montreal](https://josephpcohen.com/) + +## Citations + +Second Paper available [here](http://arxiv.org/abs/2006.11988) and [source code for baselines](https://github.com/mlmed/torchxrayvision/tree/master/scripts/covid-baselines) + +``` +COVID-19 Image Data Collection: Prospective Predictions Are the Future +Joseph Paul Cohen and Paul Morrison and Lan Dao and Karsten Roth and Tim Q Duong and Marzyeh Ghassemi +arXiv:2006.11988, https://github.com/ieee8023/covid-chestxray-dataset, 2020 +``` + +``` +@article{cohen2020covidProspective, + title={COVID-19 Image Data Collection: Prospective Predictions Are the Future}, + author={Joseph Paul Cohen and Paul Morrison and Lan Dao and Karsten Roth and Tim Q Duong and Marzyeh Ghassemi}, + journal={arXiv 2006.11988}, + url={https://github.com/ieee8023/covid-chestxray-dataset}, + year={2020} +} +``` + +Paper available [here](https://arxiv.org/abs/2003.11597) + +``` +COVID-19 image data collection, arXiv:2003.11597, 2020 +Joseph Paul Cohen and Paul Morrison and Lan Dao +https://github.com/ieee8023/covid-chestxray-dataset +``` + +``` +@article{cohen2020covid, + title={COVID-19 image data collection}, + author={Joseph Paul Cohen and Paul Morrison and Lan Dao}, + journal={arXiv 2003.11597}, + url={https://github.com/ieee8023/covid-chestxray-dataset}, + year={2020} +} +``` + +<meta name="citation_title" content="COVID-19 image data collection" /> +<meta name="citation_publication_date" content="2020" /> +<meta name="citation_author" content="Joseph Paul Cohen and Paul Morrison and Lan Dao" /> + +## License + +Each image has license specified in the metadata.csv file. Including Apache 2.0, CC BY-NC-SA 4.0, CC BY 4.0. + +The metadata.csv, scripts, and other documents are released under a CC BY-NC-SA 4.0 license. Companies are free to perform research. Beyond that contact us.