The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
COVID-19 Image Data Collection: Prospective Predictions are the Future
Open accessAbstract
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
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Cited by
Transformers in medical imaging: A survey
Shamshad, F., Khan, S., Zamir, S. W., Khan, M. H., Hayat, M., Khan, F. S., & Fu, H. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102802 · Cited by 1,296
AI for radiographic COVID-19 detection selects shortcuts over signal
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Advances in medical image analysis with vision Transformers: A comprehensive review
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COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data
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Metadata
- Authors
- Cohen, J. P., Morrison, P., Dao, L., Roth, K., Duong, T., & Ghassem, M.
- Venue
- Machine Learning for Biomedical Imaging
- Published
- 2020-12-15
- Volume
- 1
- Issue
- December 2020
- Pages
- 1-38
- ISSN
- 2766-905X (ISSN-L)
- OpenAlex
- W3036790661
- Cited by
- 927
- References
- 31
- FWCI
- 80.24
- Type
- journal article
- Language
- en
- Open access
- green
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