Cargando…
Transfer Learning with Deep Convolutional Neural Networks for Classifying Cellular Morphological Changes
The quantification and identification of cellular phenotypes from high-content microscopy images has proven to be very useful for understanding biological activity in response to different drug treatments. The traditional approach has been to use classical image analysis to quantify changes in cell...
Autores principales: | Kensert, Alexander, Harrison, Philip J., Spjuth, Ola |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6484664/ https://www.ncbi.nlm.nih.gov/pubmed/30641024 http://dx.doi.org/10.1177/2472555218818756 |
Ejemplares similares
-
Evaluation of deep convolutional neural networks in classifying human embryo images based on their morphological quality
por: Thirumalaraju, Prudhvi, et al.
Publicado: (2021) -
Deep-learning convolutional neural networks with transfer learning accurately classify COVID-19 lung infection on portable chest radiographs
por: Kikkisetti, Shreeja, et al.
Publicado: (2020) -
Evaluating parameters for ligand-based modeling with random forest on sparse data sets
por: Kensert, Alexander, et al.
Publicado: (2018) -
Transfer of Learning in the Convolutional Neural Networks on Classifying Geometric Shapes Based on Local or Global Invariants
por: Zheng, Yufeng, et al.
Publicado: (2021) -
Deep Convolutional Neural Networks for Classifying Body Constitution Based on Face Image
por: Huan, Er-Yang, et al.
Publicado: (2017)