Prof. Dr. Anja Hennemuth
Prof. Dr. Anja Hennemuth
Fellow
Fellow | BIFOLD
Professor for Image-based Therapy Support | Institute for Cardiovascular Computer-assisted Medicine, Charité – Universitätsmedizin Berlin
Member of Management Board | Fraunhofer MEVIS
| 2020 | 3rd Place EMIDEC Classification Challenge at MICCAI 2020 |
| 2017 | Best Paper Award at the conference Functional Imaging and Modeling of the Heart |
| 2015 | KUKA Best Paper Award at the conference for Computer-assisted Radiology and Surgery |
| 2014 | Fraunhofer Talenta Excellence Program |
| 2011 | Elsevier Best Paper Award at the conference Medical Image Computing and Computer Assisted Interventions |
| 2000 | sd&m Scholarship |
- Medical Image Computing
- Image-based Therapy Support
- Clinical Decision Support
- Image-based Modeling
- Machine Learning in Medical Image Processing
- GI
- ISMRM
Hinrich Rahlfs, Markus Hüllebrand, Sebastian Schmitter, Christoph Strecker, Andreas Harloff, Anja Hennemuth
Learning carotid vessel wall segmentation in black blood MRI using sparsely sampled cross-sections from 3D data
Johanna Brosig, Nina Krüger, Isaac Wamala, Matthias Ivantsits, Simon Sündermann, Jörg Kempfert, Stefan Heldmann, Anja Hennemuth
Learning 3D aortic root assessment based on sparse annotations
Antonia Popp, Alaa Abd El Al, Marie Hoffmann, Ann Laube, Peter McGranaghan, Volkmar Falk, Anja Hennemuth, Alexander Meyer
Segment-wise Evaluation in X-ray Angiography Stenosis Detection
Tabea Kossen, Manuel Alexander Hirzel, Vince Istvan Madai, Franziska Boenisch, Anja Hennemuth, Kristian Hildebrand, Sebastian Pokutta, Kartikey Sharma, Adam Hilbert, Jan Sobesky, Ivana Galinovic, Ahmed Abdelrahim Khalil, Jochen B Fiebach, Dietmar Frey
Toward sharing brain images: Differentially private TOF-MRA images with segmentation labels using generative adversarial networks
Anja Hennemuth, Markus Hüllebrand, Patrick Doeblin, Nina Krüger, Sebastian Kelle
Anwendungen von künstlicher Intelligenz in der diagnostischen kardialen Bildanalyse
An overview of the current state of research in BIFOLD
Since the official announcement of the Berlin Institute for the Foundations of Learning and Data in January 2020, BIFOLD researchers achieved a wide array of advancements in the domains of Machine Learning and Big Data Management as well as in a variety of application areas by developing new Systems and creating impactfull publications. The following summary provides an overview of recent research activities and successes.