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

By Joseph Paul Cohen, Paul Morrison, L. Dao, Karsten Roth, T. Duong, Marzyeh Ghassemi. The Journal of Machine Learning for Biomedical Imaging, 2020

848 citations recorded by the source corpus.

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

Subfields: Health Informatics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/8074bd92913e2eaef031a832126e515f80b06996

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network (2020, methodology): “On the other hand, the COVID-19 Image DataSet has been used to retrieve the COVID-19 positive samples and is available in [52].”
Transformers in Medical Image Analysis: A Review (2022, background): “[48] COVID-19 Lung Cohen’s dataset [49] COVID-19 database [47] outlooker attention 99.”
COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data (2020, methodology): “Medical observations were made by one of the co-authors of this research (Dr. Saha) who is also a medical professional, as well as by treating doctors of the COVID-19 dataset [16] patients.”
Transformers in Medical Imaging: A Survey (2022, methodology): “Experiments on COVID-19 lungs POCUS dataset [210], [211] demonstrate the effectiveness of their proposed architecture with above 90% classification accuracy.”
Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases (2021, neutral): “The entire code for our multi-classification deep learning models was uploaded to the GitHub website at [55].”
The enlightening role of explainable artificial intelligence in medical & healthcare domains: A systematic literature review (2023, methodology): “…six institutions were collected and used [120] Image [40] Cholec80 Link Video [43] LIDC-IDRI dataset Link Image [62] Chest Xray Dataset collected by[121] Link Image [76] CT volume data from four hospitals in China (Private), CC-CCII Open Image [78] MRI Alzheimer brain image dataset Open Image [33]…”
Deep Learning for Chest X-ray Analysis: A Survey (2021, background): “Several studies (Yao et al., 2019; Zech et al., 2018; Cohen et al., 2020b) demonstrated that there was a significant drop in performance when deep learning systems were tested on datasets outside their training domain for a variety of CXR applications.”