Cargando…
A deep-learning-based workflow to deal with the defocusing problem in high-throughput experiments
The increasing throughput of experiments in biomaterials research makes automatic techniques more and more necessary. Among all the characterization methods, microscopy makes fundamental contributions to biomaterials science where precisely focused images are the basis of related research. Although...
Autores principales: | Xue, Yunfan, Qian, Honglin, Li, Xu, Wang, Jing, Ren, Kefeng, Ji, Jian |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
KeAi Publishing
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8665348/ https://www.ncbi.nlm.nih.gov/pubmed/34938925 http://dx.doi.org/10.1016/j.bioactmat.2021.09.018 |
Ejemplares similares
-
Generalising from conventional pipelines using deep learning in high-throughput screening workflows
por: Garcia Santa Cruz, Beatriz, et al.
Publicado: (2022) -
Multiplexed high-throughput localized electroporation workflow with deep learning–based analysis for cell engineering
por: Patino, Cesar A., et al.
Publicado: (2022) -
PathFlowAI: A High-Throughput Workflow for Preprocessing, Deep Learning and Interpretation in Digital Pathology
por: Levy, Joshua J., et al.
Publicado: (2020) -
Defocusing nonlinear Schrödinger equations
por: Dodson, Benjamin
Publicado: (2019) -
Crowding Effects across Depth Are Fixation-Centered for Defocused Flankers and Observer-Centered for Defocused Targets
por: Eberhardt, Lisa V., et al.
Publicado: (2020)