Machine learning applications in cell image analysis
By Andrey Kan. 2017
134 citations recorded by the source corpus.
Subfields: Biophysics, Computer Vision and Pattern Recognition, Media Technology, Medical Imaging
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In Biophysics
In Computer Vision and Pattern Recognition
In Media Technology
In Medical Imaging
Citations in the corpus
- Applications of Deep Learning and Reinforcement Learning to Biological Data (2017, methodology): “, Omics (covers data from genetics and [gen/ transcript/ epigen/ prote/ metabol]omics [26]), Bioimaging (covers data from [sub-]cellular images acquired by diverse imaging techniques [27]), Medical Imaging (covers data from [medical/ clinical/ health] imaging mainly through diagnostic imaging techni”
- Reproducibility: Designing a rigorous microscopy experiment: Validating methods and avoiding bias (2019, unknown): “Machine learning approaches can be very useful for generating selection criteria and are available in free and open-source packages (Caicedo et al., 2017; Kan, 2017; http://ilastik.org/ [Sommer et al., 2011]; https://imagej.net/ Trainable_Weka_Segmentation [Arganda-Carreras et al., 2017]).”