An Adaptive Learning Model for Multiscale Texture Features in Polyp Classification via Computed Tomographic Colonography

Objective: As an effective lesion heterogeneity depiction, texture information extracted from computed tomography has become increasingly important in polyp classification. However, variation and redundancy among multiple texture descriptors render a challenging task of integrating them into a gener...

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Detalles Bibliográficos
Autores principales: Cao, Weiguo, Pomeroy, Marc J., Zhang, Shu, Tan, Jiaxing, Liang, Zhengrong, Gao, Yongfeng, Abbasi, Almas F., Pickhardt, Perry J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8840570/
https://www.ncbi.nlm.nih.gov/pubmed/35161653
http://dx.doi.org/10.3390/s22030907

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8840570/
https://www.ncbi.nlm.nih.gov/pubmed/35161653
http://dx.doi.org/10.3390/s22030907

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