Computational medical imaging and machine learning – methods, infrastructure and applications
Over the past few years there has been a dramatic development in areas associated to machine learning and artificial intelligence. This is caused by breakthroughs in “deep learning”, a collection of techniques that enable computers to uncover complicated patterns and connections in large data sets. Increased access to data (“big data”) and increased computational power has made it possible to use deep neural networks, which has become the state-of-the-art approach to many key challenges in computer vision, language modelling and robotics. These developments have enormous potential also within medicine, where large data sets from health registers, images, biopsies and gene sequences are collected.
The goal of the project, Computational medical imaging and machine learning – methods, infrastructure and applications, is to develop, implement, disseminate and evaluate machine learning techniques in the analysis of medical images and image-related data.
To successfully incorporate machine learning in medicine, doctors and medical specialists have to take a leading role. Our project, initiated from the Department of Biomedicine at UiB and the Department of Computing, Mathematics and Physics at HVL, is therefore tightly connected with departments at the hospital where data is collected and decisions are made.
The project involves many researchers in Bergen, both clinical and methodological, in addition to national and international collaborators from world-class research institutions in the USA (Mayo Clinic), Switzerland (ETH), Germany (Zuse Institute), France (ISIMA), Luxembourg (LIH) and Poland (TUL).
We will recruit new researchers, offer new interdisciplinary courses for medical students, engineers and natural science students, and disseminate and discuss methods and results with a wider audience. There’s also a substantial innovation potential for machine learning in medicine, and the project will be able to identify and potentially pursue such opportunities.
The project aims to to contribute to increased degree of personalized medicine and better decision support for diagnosis, prognosis and therapy in diseases and conditions where images are an important source of information.
Norwegian Cancer Society grant to research discovery and follow-up of lung cancer using machine learning
We are happy to announce that Renate Grüner and Erlend Hodneland have been awarded a grant by the Norwegian Cancer Society to research discovery and follow-up of lung cancer using machine learning techniques. You can read more information about this project below (in...
The machine learning seminar series: A mini-conference on machine learning @ Department of Radiology
Welcome to the sixth installment of the joint MMIV/HVL/UiB machine learning seminar series, October 17th at Haukeland University Hospital. We're organizing a mini-conference where some of the ongoing work of students associated with MMIV will be presented. See...
Today two members of the MMIV centre team, Arvid and Alexander Lundervold are featuring in a panel discussion at the Bergen public library on 'Artificial Intelligence in health care'. The discussion starts at 18:00, and more information can be found on the event...
The first meeting will be held at HVL, Wednesday February 21st, 14.15-16.00, in room D113 (Auditorium 8). The address is Kronstad, Inndalsveien 28. See this mazemap link for room location: If you are interested in attending, please fill out the doodle-link:...
We hereby wish to invite you to a new series of seminar-based meetings on machine learning, to be held at the three organising institutions, Western Norway University of Applied Sciences (HVL), University of Bergen (UiB) and Haukeland University Hospital (HUS). The...
Affiliated senior researchers
Samaneh Abolpour Mofrad
Dept. of Computing, Mathematics and Physics, Western Norway University of Applied Sciences.
Department of Biomedicine, University of Bergen