Accelerating voxelwise annotation of cross-sectional imaging through AI collaborative labeling with quality assurance and bias mitigation

BACKGROUND: precision-medicine quantitative tools for cross-sectional imaging require painstaking labeling of targets that vary considerably in volume, prohibiting scaling of data annotation efforts and supervised training to large datasets for robust and generalizable clinical performance. A straig...

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Detalles Bibliográficos
Autores principales: Dreizin, David, Zhang, Lei, Sarkar, Nathan, Bodanapally, Uttam K., Li, Guang, Hu, Jiazhen, Chen, Haomin, Khedr, Mustafa, Khetan, Udit, Campbell, Peter, Unberath, Mathias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Radiology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10362988/
https://www.ncbi.nlm.nih.gov/pubmed/37485306
http://dx.doi.org/10.3389/fradi.2023.1202412

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10362988/
https://www.ncbi.nlm.nih.gov/pubmed/37485306
http://dx.doi.org/10.3389/fradi.2023.1202412

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