Cargando…
Hagnifinder: Recovering magnification information of digital histological images using deep learning
BACKGROUND AND OBJECTIVE: Training a robust cancer diagnostic or prognostic artificial intelligent model using histology images requires a large number of representative cases with labels or annotations, which are difficult to obtain. The histology snapshots available in published papers or case rep...
Autores principales: | Zhang, Hongtai, Liu, Zaiyi, Song, Mingli, Lu, Cheng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10009300/ https://www.ncbi.nlm.nih.gov/pubmed/36923447 http://dx.doi.org/10.1016/j.jpi.2023.100302 |
Ejemplares similares
-
Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels
por: Sornapudi, Sudhir, et al.
Publicado: (2018) -
Accurate deep learning model using semi-supervised learning and Noisy Student for cervical cancer screening in low magnification images
por: Kurita, Yuki, et al.
Publicado: (2023) -
Relationship between magnification and resolution in digital pathology systems
por: Sellaro, Tiffany L., et al.
Publicado: (2013) -
Magnification Error in Digital Radiographs of the Cervical Spine Against Magnetic Resonance Imaging Measurements
por: Shigematsu, Hideki, et al.
Publicado: (2013) -
Estimation of vital signs from facial videos via video magnification and deep learning
por: Lin, Bin, et al.
Publicado: (2023)