Automated quantification of SARS-CoV-2 pneumonia with large vision model knowledge adaptation
The proposed new method has achieved the SOTA performance to quantify the severity of SARS-CoV-2 pneumonia at a significantly lower cost. The method can be extended to other infectious disease detection or quantification to expedite the application of AI in medical research.
Authors
- Zhaohui Liang (ORCID: https://orcid.org/0000-0002-9361-5535)
- Sivaramakrishnan Rajaraman (ORCID: https://orcid.org/0000-0003-0871-8634)
- Zhiyun Xue (ORCID: https://orcid.org/0000-0003-0644-385X)
- Sameer Antani (ORCID: https://orcid.org/0000-0002-0040-1387)
Institutions
- National Institutes of Health (US)
- United States National Library of Medicine (US)
Publication Details
- Journal
- New Microbes and New Infections
- Published
- 2024-08-15
- DOI
- https://doi.org/10.1016/j.nmni.2024.101457
- Citations
- 1
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.40
Funders
- National Institutes of Health
- U.S. National Library of Medicine