Honorary Guest Speaker:
Professor Sir Michael Brady to open the conference
Emeritus Professor of Oncological Imaging at the University of Oxford and a Fellow of Keble College, Oxford. Published over 600 articles, written/edited 8 books, authored 25 patents, and has started several successful companies in medical image analysis: Volpara Health Technologies Mirada Medical, Perspectum Diagnostics, ScreenPoint bv, and Optellum. Fellow of the Royal Society, the Royal Academy of Engineers, the Institute of Electrical Engineers, the Institute of Physics, and of the American Association of Artificial Intelligence. Awarded the Faraday Medal for the year 2000, and a Third Millennium medal of the IEEE. Awarded honorary doctorates by the Universities of Essex, Manchester, Liverpool, Southampton, and the Universite Paul Sabatier, Toulouse. Honorary professor at the University of Wales, Aberystwyth and Professeur Invité at INRIA, Sophia Antipolis during 1994-5 and 1996-7. Chair of MIUA 1999.
Interactive Image Segmentation and Visualisation for Cranio-Maxillofacial Surgery Planning and Orthopedic Applications
Professor Nyström is the Head of the Division of Visual Information and Interaction at the Department of Information Technology, and Director of the Centre for Image Analysis in Uppsala University, Sweden. Vice-Chair of the Council for Research Infrastructure in Sweden.
Model-based approach to 3D shape recovery and analysis
Professor Park, from the School of Computing at the Korea Advanced Institute of Science and Technology (KAIST, Daejeon, South Korea) is the Head of the Computer Graphics and Visualisation Research Laboratory and Head of KAIST Research Group of Future Emerging Technology on Medical Imaging.
Machine Learning for Medical Imaging
Professor Rueckert is the Head of the Biomedical Image Analysis Laboratory at the Imperial College London, Fellow of the Royal Academy of Engineering, MICCAI and IEEE Societies and Chair of MIUA 2004.
Tutorial 1: Processing and analysing microscopy images with MATLAB
Senior Lecturer in the Department of Electrical and Electronic Engineering at the City University London, Senior member of the IEEE. Chair of MIUA 2014. Author of “Biomedical Image Analysis Recipes in MATLAB: for Life Scientists and Engineers” (ISBN 978-1-118-65755-3, Wiley-Blackwell
In this tutorial, we will analyse some common aspects of biomedical images that have been acquired through different modes of microscopy. The tutorial will be divided in two parts (estimated duration time: 45 minutes each). The first will concentrate on the acquisition; some the advantages and disadvantages of the different modalities like Phase Contrast, Fluorescence, Immunohistochemistry and the processing of these modalities. In the second, we will apply different useful algorithms to analyse microscopy images, address common problems as well as introducing some important aspects of MATLAB, from using writing reports (or even books!) to generating publication-quality images with MATLAB (you will never need Illustrator again).
Dr Jasmina Lazic
Jasmina Lazic works on MathsWorks collaborations with academia in the UK, specialising in Data Science and Big Data. Before joining MathsWorks in 2011, Jasmina held a number of academic positions focused on Research in global optimisation and heuristic design. She has had publications in the areas of mixed-integer programming and clustering. Jasmina has a Ph.D. in mathematics from Brunel University and a diploma degree in mathematics and computer science from the University of Belgrade in Serbia.
Tutorial 2: Deep Learning on the analysis of radiological images
Post-Doctoral Researcher in the Department of Computing at the Imperial College London, former Software Engineer and Microsoft Research Intern, and author of DeepMedic