Home
Uni-Logo
 

Deep learning for automated image analysis in generic imaging modalities

Project within the BIOSS Cluster of Excellence (EXC 294)


Project members

Prof. Dr. Thomas Brox
Dr. Thorsten Falk
Maxim Tatarchenko


Abstract

We will develop new deep learning methods in order to cover a broader set of tasks in biomedical data analysis. Concrete challenges to be approached will be the full applicability of deep learning to image sequences and volumetric images. We will also work on the challenge to obtain good results with little training data given for a particular task. To this end, we will investigate the relationship between deep networks trained for different tasks and a generic deep network trained on all of them jointly.

Deep network architecture for semantic segmentation
Image segmentation with a deep network. The network can be trained for many similar biomedical segmentation tasks. In this project, we want to extend the framework to image sequences and volumetric images.

Publications


Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, LNCS, Vol.9901: 424--432, Oct 2016

Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer LNCS Vol.9351:234-241, 2015