• Computer vision and machine learning methods
• Development of methods for application factors in computer vision, such as interpretability, managing high-resolution images, inclusion of domain knowledge, etc.
• Applying deep learning to vision problems in digital health

• Large-Scale Medical Image Analysis
• Organizing and conducting workshops on artificial intelligence for small and medium-sized companies as AI expert in the Mittelstand 4.0-Kompetenzzentrum Berlin

• Short course 20/21: Introduction data science and python for digital health 
• Master project 20/21: Digitizing and analyzing historic patient files using deep learning
• Lecture 20: Deep Learning
• Master project 20: Medical imaging model zoo
• Lecture 19/20: Data management for digital health
• Master project 19/20: Learning from population-scale medical imaging data without labels
• Bachelor project 19/20: Usable active learning tool for 3D image segmentation in life science and medical applications
• Seminar 19: Applied machine learning for digital health
• Lecture 18/19: Data management for digital health
• Master project 18/19: Visualisation and analysis of learned features from deep convolutional networks in breast cancer
• Master project 18: Molecular Tumor Board

Ongoing

• Martin Gerstmaier, M.Sc. IT Systems Engineering, HPI, Self-Attention in Medical Imaging 
• Marius Pullig, M.Sc., TU Berlin, Deep learning models for 3D MRI brain classification: a multi-sequence comparison
• DuyHung Nguyen, M.Sc. IT Systems Engineering, HPI, Building a Text Embedding Model for the Imaging Research Warehouse at Mount Sinai
• Tim Henning, M.Sc. IT Systems Engineering, HPI, HistoFlow: Label-Efficient and Interactive Deep Learning Cell Analysis

2019

• Ahmed Rekik (M.Sc. IT Systems Engineering, HPI), Learning liver and tumor segmentation with small annotated datasets

• M.Sc. Digital Engineering, Otto-von-Guericke-Universität Magdeburg (2018)
  • Grade: 1.3, best of class 2018
  • Thesis: Automatic Landmark Detection for Preoperative Planning of Hip Surgery using Image Processing and Convolutional Neural Networks, in collaboration with Sectra Medical (Linköping, Sweden), Link
• Semester abroad, Information systems, Universidade Federal de Santa Catarina, Brazil (2017)
• B.Sc. Industrial Engineering, Otto-von-Guericke-Universität Magdeburg (2015)
  • Grade: 1.8
  • Thesis: Design and development of a Car2Car communication interface for
    autonomous vehicles

• Da Cruz, H. F., Bergner, B., Konak, O., Schneider, F., Bode, P., Lempert, C., & Schapranow, M. P. (2019, October). MORPHER-A Platform to Support Modeling of Outcome and Risk Prediction in Health Research. In 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) (pp. 462-469). IEEE Computer Society. Link
• Oleynik, M., Faessler, E., Sasso, A. M., Kappattanavar, A., Bergner, B., Da Cruz, H. F., ... & Böttinger, E. P. (2018, November). HPI-DHC at TREC 2018 Precision Medicine Track. In TREC. Link
• Bergner, B., & Krempl, G. (2016). Active Subtopic Detection in Multitopic Data. In AL@ iKNOW (pp. 35- 44). Link

• Taleb, A., Loetzsch, W., Danz, N., Severin, J., Gaertner, T., Bergner, B., & Lippert, C. (2020). 3D Self-Supervised Methods for Medical Imaging. arXiv preprint arXiv:2006.03829. Link
• Henning, T., Bergner, B., & Lippert, C. (2020). HistoFlow: Label-Efficient and Interactive Deep Learning Cell Analysis. bioRxiv. Link