Title: "Deep Learning in Medical Imaging"
Speaker: Eliott Brion
Location: "Shannon" Seminar Room, Place du Levant 3, Maxwell Building, 1st floor
Date / Time (duration): Wednesday 29/03/2017, 16h15 (~45')
Abstract: To illustrate its use in medical imaging, the goal of this seminar is to show how deep learning can automatically contour healthy organs and tumors in CT scans.
As 3 in 10 Belgians will develop cancer before their 75th birthday, we must improve treatment. Proton therapy is a promising treatment since it kills cancerous cells with high accuracy, leaving the neighboring healthy organs undamaged. However, its wider adoption is still hampered by two challenges: the uncertainty of the protonís energy deposition along its path (or density changes) and the uncertainty in targetís position (the geometrical changes). We will focus on this second challenge; for which fast, robust and autonomous (i.e. with minimal external user intervention) contouring is critical. The good news is that a new set of algorithms called deep learning now allows to do that. Roughly speaking, deep learning works by learning representations of already contoured images with multiple levels of abstraction. We will show how it has been successfully applied in recent research and why the access to labeled data is so crucial.
Last updated October 02, 2017, at 05:13 PM