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Getting numbers out of cells - applications of deep neural networks to microscopy image compendia
High throughput microscopy generates high-dimensional data that are far from straightforward to analyze. I will describe our work in using deep neural networks to derive qualitative descriptors (e.g. subcellular localization of a fluorescent protein, or tissue of a histopathology image) and quantitative features (such as abundance of a tagged protein in cell membrane) from images. We apply these ideas to the publicly available GTEX tissue histology dataset, and yeast GFP collection micrographs.
Date:
17 May 2017, 11:30
Venue:
Big Data Institute, Old Road Campus OX3 7LF
Venue Details:
BDI seminar room
Speaker:
Dr Leopold Parts (Sanger Institute)
Organiser:
Carol Mulligan-John (University of Oxford)
Part of:
BDI seminars
Booking required?:
Not required
Audience:
Members of the University only
Editors:
Graham Bagley,
Natasha Bowyer
