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
Julia Mellert, Julius Kaemmel, Joerg Kempfert, Volker Markl, Simon Sündermann, Steffen Zeuch, Olaf Hellwich, Anja Hennemuth
Synergies and Conflicts in Multi-Task Surgical Vision: Stereo Matching, Segmentation, and Classification of Stereoendoscopic Video Frames in Mitral Valve Surgery
Antonia Popp, Alaa Abd El Al, Marie Hoffmann, Ann Laube, Jörg Kempfert, Anja Hennemuth, Alexander Meyer
Multi-view and multi-frame voting-based stenosis detection and quantification in X-ray coronary angiography
Julia Mellert, Lisa Bautz, Johanna Brosig, Olaf Hellwich, Anja Hennemuth
Multimodal Mitral Valve Segmentation: Mesh-Based 3D and Depth-Transfer Learning
Héloïse Bustin, Tom Meyer, Jakob Jordan, Lars Walczak, Heiko Tzschätzsch, Ingolf Sack, Anja Hennemuth
Multifrequency Neural Network-based Wave Inversion in MR Elastography with Uncertainty Quantification
Antonia Popp, Alaa Abd El Al, Marie Hoffmann, Ann Laube, Jörg Kempfert, Anja Hennemuth, Alexander Meyer
Uncertainty in automated stenosis quantification using multiview x-ray coronary angiography videos
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.