Deep Learning in Medical Imaging

In many of our projects, large collections of data emerge. Those big data sets stem from population-based studies, interdisciplinary clinical research projects, or simply have accumulated over time. Using state-of-the-art machine learning techniques like Convolutional Neural Networks and other Deep Learning architectures, we explore the wealth of information contained in this clinical data. In close collaboration with the DIAG group in Nijmegen (NL), our aim is to automate data analysis processes in areas where computer intelligence can relieve doctors from repetitive tasks.

Main Features

 

  • Advanced Computer Vision with Object-Based Image Analysis
  • Feature Learning in Medical Data using Deep Learning
  • Explorative and Model-Free Multi-omics Data Analysis
  • Integrating Clinical Data Sources with Algorithmic Developments for Automation

 

Applications

 

  • Salmonella detection in volume electron microscopy images
  • Analysis of histology images
  • Analysis of ophthalmology images
  • Lung CAD
  • Quantitative Follow-up for Oncology