Machine learning and computer vision approaches for phenotypic profiling

By Ben T Grys, Dara S Lo, Nil Sahin, Oren Z. Kraus, Quaid D. Morris, Charles Boone, Brenda J. Andrews. 2017

154 citations recorded by the source corpus.

Grys et al. review computer vision and machine-learning methods that have been applied to phenotypic profiling of image-based data. Descriptions are provided for segmentation, feature extraction, selection, and dimensionality reduction, as well as clustering, outlier detection, and classification of data.

Subfields: Biophysics, Media Technology, Molecular Biology

Citations in the corpus

• Intelligent Image-Activated Cell Sorting. (2018, unknown): “Selection of digital algorithms is highly flexible within the range of the specified processing time, thereby rendering previously developed image-processing algorithms (including machine learning) for microscopy and imaging flow cytometry platforms (Caicedo et al., 2017; Basiji et al., 2007; Lee et”