From Embeddings to Accuracy: Comparing Foundation Models for Radiographic Classification.

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Bibliographic Details
Title: From Embeddings to Accuracy: Comparing Foundation Models for Radiographic Classification.
Authors: Li X; Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA. xue.li@wisc.edu., Merkow J; Microsoft Health and Life Sciences, Redmond, WA, USA., Codella NCF; Microsoft Health and Life Sciences, Redmond, WA, USA., Santamaria-Pang A; Microsoft Health and Life Sciences, John Hopkins Medicine, Redmond, WA, USA., Sangani N; Microsoft Health and Life Sciences, Redmond, WA, USA., Ersoy A; Microsoft Health and Life Sciences, Redmond, WA, USA., Burt C; Microsoft Health and Life Sciences, Redmond, WA, USA., Garrett JW; Department of Radiology, Department of Medical Physics, Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA., Bruce RJ; Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA., Warner JD; Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA., Bradshaw T; Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA., Tarapov I; Microsoft Health and Life Sciences, Redmond, WA, USA., Lungren MP; Microsoft Health and Life Sciences, Redmond, WA, USA., McMillan AB; Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA.
Source: Journal of imaging informatics in medicine [J Imaging Inform Med] 2025 Dec 02. Date of Electronic Publication: 2025 Dec 02.
Publication Type: Journal Article
Journal Info: Publisher: Springer Nature Country of Publication: Switzerland NLM ID: 9918663679206676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2948-2933 (Electronic) Linking ISSN: 29482925 NLM ISO Abbreviation: J Imaging Inform Med Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2948-2933
DOI:10.1007/s10278-025-01747-5