BDI Seminar - Interpretable machine learning with tensor models for healthcare
Big, multidimensional data such as brain/heart MRI is expected to transform healthcare. However, such data poses great challenges, particularly the need for interpretation and very high dimensionality accompanied by a relatively small sample size. Deep learning models are powerful but inadequate to address these problems, due to their opaque and data-hungry nature. This talk will present tensor-based machine learning models for extracting/selecting compact, interpretable features directly from tensor representations of multidimensional data. I will show their applications in prediction and interpretation of brain fMRI for neural decoding and cardiac MRI for disease diagnosis. Finally, I will discuss some ongoing and future research works on interpretable machine learning, transfer learning, and network embedding.
Date:
29 March 2019, 12:30 (Friday, 11th week, Hilary 2019)
Venue:
Big Data Institute (NDM), Old Road Campus OX3 7LF
Venue Details:
Seminar Room 1
Speaker:
Haiping Lu (University of Sheffield)
Organising department:
Big Data Institute (NDM)
Organiser:
Carol Mulligan-John (University of Oxford)
Part of:
BDI seminars
Booking required?:
Not required
Audience:
Members of the University only
Editor:
Hannah Freeman
