Upload 931 files

.gitattributes

@@ -58,3 +58,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+images/16745_2_1.PNG filter=lfs diff=lfs merge=lfs -text
+images/16745_2_2.PNG filter=lfs diff=lfs merge=lfs -text
+images/16745_2_3.PNG filter=lfs diff=lfs merge=lfs -text
+images/16745_7_5.PNG filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,168 @@
+
+#### 🛑 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.