Learning Clinical information from Medical Images

Speaker:        Professor Daniel Rueckert
                Department of Computing
                Imperial College London, U.K.

Title:          "Learning Clinical information from Medical Images"

Date:           Thursday, 31 March 2016

Time:           4:00pm - 5:00pm

Venue:          Room 3501 (via lifts 25 or 26), HKUST

Abstract:

This talk will focus on the convergence medical imaging and machine
learning techniques for the discovery and quantification of clinically
useful information from medical images. The first part of the talk will
describe machine learning techniques based on sparsity that can be used
for image reconstruction, e.g. the acceleration of MR imaging. The second
part will discuss model-based approaches that employ statistical as well
as probabilistic approaches for segmentation. In particular, we will focus
on segmentation techniques that combine patch-based approaches such as
dictionary learning with sparsity to improve the accuracy and robustness
of the segmentation approaches.


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Biography:

Professor Daniel Rueckert is Professor of Visual Information Processing
and heads the Biomedical Image Analysis group at the Department of
Computing, Imperial College London, UK. Professor Rueckert is the Deputy
Head of Department. Professor Rueckert is an associate editor of IEEE
Transactions on Medical Imaging, Editorial Board Members of Medical Image
Analysis, as well as Image & Vision Computing. In 2015, he was elected as
a Fellow of the Royal Academy of Engineering and as Fellow of the IEEE.