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
Ahmed Rekik (M.Sc. IT Systems Engineering, HPI), Learning liver and tumor segmentation with small annotated datasets
Education
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)
Thesis: Design and development of a Car2Car communication interface for autonomous vehicles
Publications
Peer-reviewed articles
Sommerfeld, Romeo, et al. "Abstract P305: MACHINE LEARNING DETECTS ASSOCIATIONS BETWEEN RETINA FEATURES AND BLOOD PRESSURE STATUS IN RICE DIET PROGRAM PATIENTS." Hypertension 79.Suppl_1 (2022): AP305-AP305. Link
Burmeister, Josafat-Mattias, et al. "Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentation." Link
Taleb, Aiham, et al. "Self-Supervised Learning Methods for Label-Efficient Dental Caries Classification." Diagnostics 12.5 (2022): 1237. Link
Pullig, Marius, et al. "Deep Learning Models for 3D MRI Brain Classification." Bildverarbeitung für die Medizin 2022. Springer Vieweg, Wiesbaden, 2022. 204-209. Link
Bergner, Benjamin, et al. "Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-rays." Medical Imaging with Deep Learning. 2021. Link
Henkenjohann, Richard, et al. "An Engineering Approach Towards Multi-site Virtual Molecular Tumor Board Software." International Conference on ICT for Health, Accessibility and Wellbeing. Springer, Cham, 2021.
Taleb, Aiham, et al. "3d self-supervised methods for medical imaging." Advances in Neural Information Processing Systems 33 (2020): 18158-18172. Link
Da Cruz, Harry Freitas, et al. "MORPHER-A Platform to Support Modeling of Outcome and Risk Prediction in Health Research." 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2019. Link
Oleynik, Michel, et al. "HPI-DHC at TREC 2018 Precision Medicine Track." TREC. 2018. Link
Bergner, Benjamin, and Georg Krempl. "Active Subtopic Detection in Multitopic Data." AL@ iKNOW. 2016. Link
Preprints
Bergner, Benjamin, Christoph Lippert, and Aravindh Mahendran. "Iterative Patch Selection for High-Resolution Image Recognition." arXiv preprint arXiv:2210.13007 (2022). Link
Bergner, Benjamin, and Christoph Lippert. "The Regularizing Effect of Different Output Layer Designs in Deep Neural Networks." (2021). Link
Henning, Tim, Benjamin Bergner, and Christoph Lippert. "HistoFlow: Label-Efficient and Interactive Deep Learning Cell Analysis." bioRxiv (2020). Link
Contact
Chair Representative:
Prof. Dr. Christoph Lippert Professor for Digital Health & Machine Learning Room: G-2.1.23 Tel.: +49-(0)331 5509-4850 E-Mail: office-lippert(at)hpi.de
Office:
Campus III, Haus G2 Room: G-2.1.22 Tel.: +49-(0)331 5509-4850 Fax: +49-(0)331 5509-4849 E-Mail: office-lippert(at)hpi.de
Visiting address:
Campus III Building G2 Rudolf-Breitscheid-Straße 187 14482 Potsdam, Germany
