Development of deepometry for the supervised and weakly supervised learning of imaging data

Imaging flow cytometry offers the opportunity to image millions of cells in a short period of time, it is a transformative technology with the potential to diagnose a host of diseases. This project will use the latest algorithms and analytic techniques to extract useful information from large datasets generated using this system for a host of clinical collaborations. Specifically, the project builds on, Deepometry, an open-source workflow developed with the objective of applying deep learning algorithms along with single cell analytics to the analysis of cytometry data which was developed in collaboration with GSK and the Broad Institute of MIT and Harvard. 'Deepometry 2' aims to develop our current software for use on both 2D and 3D images and to use the technology to image tissue from patient samples. This project will involve software development and the application of this deep learning tool to a number of exciting datasets.