Hasso-Plattner-Institut
Prof. Dr. Christoph Lippert
 

Benjamin Bergner, M.Sc.

PhD Student

Phone: +49 331 5509-4823


Room: Campus III - Building G2 - G-2.1.34


Email: benjamin.bergner(at)hpi.de


Web: LinkedIn -- Twitter -- Researchgate -- Google Scholar

Research Interests

  • Attention mechanisms for deep learning
  • Multiple instance learning
  • Interpretability
  • Continual learning
  • Self-supervised learning
  • Medical imaging and computer vision

Thesis supervision

  • 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)
  • 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

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