Medical Imaging meets NIPS
Saturday, December 9th - Long Beach Convention Center, USA
Speakers
Olivier Pauly
Senior research scientist in the AI team at Siemens Healthineers. His early work helped popularising ML in medical image computing. His team specializes in using large collections of data to build Artificial Intelligence applications for healthcare.
Tanveer Syeda-Mahmood
IBM Fellow and Chief Scientist/overall lead for the Medical Sieve Radiology Grand Challenge project aiming to develop automated radiology and cardiology assistants of the future that help clinicians in their decision making.
Raj Jena
Neuro-oncologist at the Addenbrooke's Hospital, University of Cambridge and clinical consultant for Microsoft Research. He provides radiotherapy and drug treatments for tumors of the brain and spine, and is an enthusiast in applying machine learning for developing new treatment methods.
Ivana Isgum
Associate Professor at UMC Utrecht where she is developing methods for automatic calcium scoring and their application to large scale screening trials. She is also working on projects related to automatic segmentation of the developing neonatal brain with MRI.
Daniel Rueckert
Professor at Imperial College London, Head of the Biomedical Image Analysis Group, and Fellow of the Royal Academy of Engineering. His research focuses on the development of computation tools using machine learning for extracting clinically useful information from medical images.
Wiro Niessen
President of the MICCAI Society and Professor of Biomedical Image Processing at Erasmus MC. His research interests include many aspects of computer vision, biomedical image analysis, and computer assisted interventions.
Kersten Petersen
Senior Medical Imaging Researcher at HeartFlow. His current research focus is on enabling state-of-the-art non-invasive diagnosis and treatment planning of cardiovascular disease using machine learning methods.
Gael Varoquaux
Brain imaging researcher at INRIA and INSERM who is using machine learning to link cognition with brain activity. He is one of the core contributors of the popular and widely used machine learning software toolkits scikit-learn and nilearn which are aiming to make advanced machine learning techniques easy for neuroimaging research.
Bjoern Menze
Professor at the Technical University of Munich with a research focus in the field of medical image computing. He develops algorithms that analyze biomedical images using models from computational physiology and biophysics. He initiated the Brain Tumour Segmentation Challenge (BraTS), which is now one of the biggest and most popular computational challenges on medical image analysis.
Yaroslav Nikulin
Research Scientist at Therapixel and winner of the Digital Mammography DREAM Challenge. He is working on automating the analysis of radiological data using latest advances in artificial intelligence, in particular deep learning.
Jorge Cardoso
Lecturer in Quantitative Neuroradiology at the Translational Imaging Group at University College London. His research explores novel highly accurate and robust machine learning techniques to segment, parcellate and localize different types of tissues using anatomical, microstructural and functional images.