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dc.contributor.authorLiu, Tianming
dc.contributor.authorSiegel, Eliot
dc.contributor.authorShen, Dinggang
dc.date.accessioned2022-06-21T11:59:54Z
dc.date.available2022-06-21T11:59:54Z
dc.date.issued2022-03-22
dc.identifier.urihttp://hdl.handle.net/10713/19202
dc.description.abstractThe coronavirus disease 2019 (COVID-19) pandemic has imposed dramatic challenges to health-care organizations worldwide. To combat the global crisis, the use of thoracic imaging has played a major role in the diagnosis, prediction, and management of COVID-19 patients with moderate to severe symptoms or with evidence of worsening respiratory status. In response, the medical image analysis community acted quickly to develop and disseminate deep learning models and tools to meet the urgent need of managing and interpreting large amounts of COVID-19 imaging data. This review aims to not only summarize existing deep learning and medical image analysis methods but also offer in-depth discussions and recommendations for future investigations. We believe that the wide availability of high-quality, curated, and benchmarked COVID-19 imaging data sets offers the great promise of a transformative test bed to develop, validate, and disseminate novel deep learning methods in the frontiers of data science and artificial intelligence.en_US
dc.description.urihttps://doi.org/10.1146/annurev-bioeng-110220-012203en_US
dc.language.isoenen_US
dc.publisherAnnual Reviews Inc.en_US
dc.relation.ispartofAnnual Review of Biomedical Engineeringen_US
dc.subjectCOVID-19en_US
dc.subjectdeep learningen_US
dc.subjectmedical image analysisen_US
dc.subjectmedical imagingen_US
dc.subjectradiologyen_US
dc.titleDeep Learning and Medical Image Analysis for COVID-19 Diagnosis and Prediction.en_US
dc.typeArticleen_US
dc.identifier.doi10.1146/annurev-bioeng-110220-012203
dc.identifier.pmid35316609
dc.source.journaltitleAnnual review of biomedical engineering
dc.source.volume24
dc.source.beginpage179
dc.source.endpage201
dc.source.countryUnited States


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