Computer vision for high content screening

By Oren Z. Kraus, Brendan J. Frey. 2016

52 citations recorded by the source corpus.

Subfields: Biophysics, Media Technology, Molecular Biology

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Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Given the dramatic successes of deep learning in biological imaging, we simply refer to articles that review recent advancements [16,202,203].”
Machine learning and computer vision approaches for phenotypic profiling (2017, methodology): “Toward the generation of phenotypic profiles, individual objects of interest, such as cells or whole organisms, must first be identified and distinguished from the background of an image, in a process called segmentation (Bengtsson et al., 2004; Meijering, 2012; Kraus and Frey, 2016).”
Applications in image-based profiling of perturbations. (2016, methodology): “These methods are already common practice for solving complex computer vision tasks [52], and are also starting to be applied in the bioimage informatics community [53,54].”
Machine learning applications in cell image analysis (2017, methodology): “Basic clustering algorithms, such as k-means and hierarchical clustering, have been frequently covered, 4,5 and pitfalls of high-dimensional clustering in biological research have been discussed by Ronan et al. 21”
Deep learning in bioinformatics (2016, unknown): “Studies of popular high content screening [148], which involves quantifying microscopic images for cell biology, are covered in the former groups [128, 134, 137].”
Data-analysis strategies for image-based cell profiling (2017, unknown): “In either case, the practice of sharing code is highly valued, to ensure rapid implementation of techniques, optimization of pipelines, and reproducibility of the results by others.”
Automating Morphological Profiling with Generic Deep Convolutional Networks (2016, background): “Modern microscopy and automation technologies enable large-scale experiments which can produce millions of cell images per day [7].”
Classifying and segmenting microscopy images with deep multiple instance learning (2015, background): “The same network can also produce an image level classiﬁcation over images of arbitrary size and varying number of cells through a MIL pooling layer.”