Combining computational modelling, deep generative learning and imaging to infer new biology
Deep learning algorithms provide unprecedented opportunities to characterise complex structure in large data, but typically in a manner that cannot easily be interpreted beyond the ‘black box’. We are developing methods to leverage the benefits of deep generative learning and computational modelling (e.g. fluid dynamics, solid mechanics, biochemistry), particularly in conjunction with biomedical imaging, to enable new insights into disease to be made. In this talk, I will describe our applications in several areas, including modelling drug delivery in cancer and retinal blood vessel loss in diabetes, and how this is leading us into the development of personalised digital twins.
Date:
24 January 2025, 11:00
Venue:
Mathematical Institute, Woodstock Road OX2 6GG
Venue Details:
L4
Speaker:
Prof Simon Walker-Samuel (UCL)
Organising department:
Mathematical Institute
Organiser contact email address:
jolliffe@maths.ox.ac.uk
Host:
Dr Eamonn Gaffney (University of Oxford)
Part of:
Mathematical Biology and Ecology
Booking required?:
Not required
Audience:
Members of the University only
Editor:
Sara Jolliffe
