Machine Learning for dynamic microscopic imaging

In the proposed exchange project, we plan to develop a new collaborative partnership that integrates the expertise of advanced optical imaging instrumentation (McMaster: Fang) and image sequence analysis (INRIA: Kervrann) towards new dynamic imaging technologies. Specifically, we are interested in optofluidics imaging flowcytometers for point-of-care infectious diseases diagnosis as well as high throughput protein-protein interaction measurements in live cell imaging. A key novelty of this project is, instead of relying static 2D images and morphological features, we acquire 3D videos of the particulates in the flow and use motion analysis to perform detection and classification. The bilateral exchange allows the Canadian student to learn advanced AI based image sequence analysis for better optimization of the instrumentation design. It would also allow the French student to get expose to new microscopy instrumentation technologies that may lead t new computation algorithms for such systems.

Faculty Supervisor:

Qiyin Fang

Student:

Partner:

Inria Rennes - Bretagne Atlantique Research Centre

Discipline:

Computer science

Sector:

Artificial Intelligence; Biotechnology; Health and Related Sciences & Technology

University:

McMaster University

Program: