Special Session 1: Wednesday, 13 April (4:20pm-5:50pm)
Organizers
Kanwal Bhatia, Herve Lombaert, Sarah Parisot, Jonathan Passerat-Palmbach
Speakers
- William M. Wells III (Professor of Radiology, Harvard Medical School)
- Flora Gilboa-Solomon (Manager of Medical Imaging Analytics, IBM Research, Haifa)
- Tal Arbel (Associate Professor, iResearch Director, Probabilistic Vision Group, and Electrical & Computer Engineering “Medical Imaging Lab”, McGill University)
- Georg Langs (Associate Professor and Head, Computational Image Analysis and Radiology Lab (CIR), Medical University of Vienna)
Keywords
machine learning, computer-aided detection and diagnosis (CAD), data mining
Abstract
Recent years have seen an increasing volume of medical image data and annotations being collected and stored. Collaborative, worldwide initiatives have begun the acquisition of hundreds of terabytes of data to be made available to the scientific community. Processing these large datasets is key to providing a wealth of information with the potential to be usefully harnessed. Along with the new clinical opportunities arising, novel image and data processing algorithms are required for working with, and learning from, large scale datasets. This Special Session aims to examine recent progress in the field, together with new openings stemming from increased data availability, as well as the specific challenges involved. To discuss these ideas, we have exciting speakers leading the Big Data drive in medical imaging. Their medical imaging and computer vision expertise is supplemented by particular experience in Big Data topics including multicentre clinical trials, large scale image search, and connectomics.
