Keywords: Machine Learning for Medical Applications
Abstract
Automatic localization of multiple anatomical structures in medical images provides important semantic information with potential benefits to diverse clinical applications. In this project, we investigate hierachical regression methods based on Random Forests and Random Ferns. Such hierarchical approaches permit to subdivide efficiently the feature space and to create a partition over it. In each cell of the resulting partition, data can be easily modeled using simple mathematical models such as constant or linear. The combination of these models over the whole partition results then in a complex non-linear model.
Pictures
|
|
Figure 1: Regression Forests: toy example
|
|
Publications
Team
Contact Person(s)
Working Group
Location

Visit our lab at Garching.
internal project page
Please contact Olivier Pauly for available student projects within this research project.