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

Author:

Cohen Joseph Paul1ORCID,Morrison Paul2,Dao Lan3ORCID,Roth Karsten456ORCID,Duong Tim7ORCID,Ghassem Marzyeh8ORCID

Affiliation:

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

Abstract

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: https://github.com/ieee8023/covid-chestxray-dataset

Publisher

Machine Learning for Biomedical Imaging

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