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

Backgroundprecision-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 straight...

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Detalhes bibliográficos

Principais autores:	David Dreizin, Lei Zhang, Nathan Sarkar, Uttam K. Bodanapally, Guang Li, Jiazhen Hu, Haomin Chen, Mustafa Khedr, Udit Khetan, Peter Campbell, Mathias Unberath
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Frontiers Media S.A. 2023-07-01
coleção:	Frontiers in Radiology
Assuntos:	AI assisted annotation AI-collaborative labeling artificial intelligence—AI CT volumetry computed tomography trauma
Acesso em linha:	https://www.frontiersin.org/articles/10.3389/fradi.2023.1202412/full
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