COVID-19 Image Data Collection: Prospective Predictions are the Future

Joseph Paul Cohen10000-0002-1334-3059, Paul Morrison2, Lan Dao30000-0001-9761-5615, Karsten Roth4,5,60000-0003-1510-7217, Tim Duong70000-0001-6403-2827, Marzyeh Ghassem80000-0001-6349-7251
1: Mila, University of Montreal, 2: Mila, Fontbonne University, 3: Department of Medicine, Mila, University of Montreal, 4: Vector, 5: Mila, 6: Heidelberg University, 7: Stony Brook Medicine, 8: Vector, University of Toronto
Publication date: 2020/12/15
PDF · Dataset · arXiv


Across the world’s coronavirus disease 2019 (COVID-19) hot spots, the need to streamline patient diagnosis and management has become more pressing than ever. As one of the main imaging tools, chest X-rays (CXRs) are common, fast, non-invasive, relatively cheap, and potentially bedside to monitor the progression of the disease. This paper describes the first public COVID-19 image data collection as well as a preliminary exploration of possible use cases for the data. This dataset currently contains hundreds of frontal view X-rays and is the largest public resource for COVID-19 image and prognostic data, making it a necessary resource to develop and evaluate tools to aid in the treatment of COVID-19. It was manually aggregated from publication figures as well as various web based repositories into a machine learning (ML) friendly format with accompanying dataloader code. We collected frontal and lateral view imagery and metadata such as the time since first symptoms, intensive care unit (ICU) status, survival status, intubation status, or hospital location. We present multiple possible use cases for the data such as predicting the need for the ICU, predicting patient survival, and understanding a patient’s trajectory during treatment. Data can be accessed here:


dataset · covid-19 · machine learning · chest x-ray · computer vision

Bibtex @article{melba:2020:002:cohen, title = "COVID-19 Image Data Collection: Prospective Predictions are the Future", author = "Cohen, Joseph Paul and Morrison, Paul and Dao, Lan and Roth, Karsten and Duong, Tim and Ghassem, Marzyeh", journal = "Machine Learning for Biomedical Imaging", volume = "1", issue = "December 2020 issue", year = "2020", pages = "1--38", issn = "2766-905X", doi = "", url = "" }
RISTY - JOUR AU - Cohen, Joseph Paul AU - Morrison, Paul AU - Dao, Lan AU - Roth, Karsten AU - Duong, Tim AU - Ghassem, Marzyeh PY - 2020 TI - COVID-19 Image Data Collection: Prospective Predictions are the Future T2 - Machine Learning for Biomedical Imaging VL - 1 IS - December 2020 issue SP - 1 EP - 38 SN - 2766-905X DO - UR - ER -

2020:002 cover