Marcel Bengs

Marcel Bengs

 

 

 

 

 

 

  Research Assistant

+49 (0)40 42878 3389 

E.3083

  marcel.bengstuhh.de

2019 - today MTEC
2016 - 2018 Northern Institute of Technology Management
2011 - 2018 Hamburg University of Technology

Interests

  • Deep Learning for Medical Image Analysis
  • 4D Spatio-Temporal Deep Learning Architectures

Roles

  • Research assistant
  • PhD student

Publications

2020

  • A. Rogalla, S. Lehmann, M. Neidhardt, J. Sprenger, M. Bengs, A. Schlaefer, S. Schupp (2020). Synthesizing Strategies for Needle Steering in Gelatin Phantoms. Models for Formal Analysis of Real Systems (MARS 2020) accepted. [BibTex]

  • N. Gessert, M. Bengs, J. Krüger, R. Opfer, A.-C. Ostwaldt, P. Manogaran, S. Schippling, A. Schlaefer (2020). 4D Deep Learning for Multiple-Sclerosis Lesion Activity Segmentation. Medical Imaging with Deep Learning accepted. [Abstract] [www] [BibTex]

  • M. Bengs, N. Gessert, A. Schlaefer (2020). A Deep Learning Approach for Motion Forecasting Using 4D OCT Data. International Conference on Medical Imaging with Deep Learning accepted. [Abstract] [www] [BibTex]

  • M. Bengs, N. Gessert, M. Schlüter, A. Schlaefer (2020). 4D Spatio-Temporal Deep Learning Methods for Motion Estimation Using 4D OCT Image Data. accepted. [Abstract] [www] [BibTex]

  • M. Neidhardt*, M. Bengs*, S. Latus, M. Schlüter, T. Saathoff, A. Schlaefer (2020). 4D Deep learning for real-time volumetric optical coherence elastography. Computer Assisted Radiology and Surgery 2020 accepted. [BibTex]

  • N. Gessert, M. Bengs, A. Schlaefer (2020). Melanoma detection with electrical impedance spectroscopy and dermoscopy using joint deep learning models. SPIE Medical Imaging 2020: Computer-Aided Diagnosis. 11314 1131414. [Abstract] [www] [BibTex]

  • M. Bengs, S. Westermann, N. Gessert, D. Eggert, A. O. H. Gerstner, N. A. Mueller, C. Betz, W. Laffers, A. Schlaefer (2020). Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection. SPIE Medical Imaging 2020: Computer-Aided Diagnosis. in print. [BibTex]

  • M. Neidhardt*, M. Bengs*, S. Latus, M. Schlüter, T. Saathoff, A. Schlaefer (2020). Deep Learning for High Speed Optical Coherence Elastography. IEEE International Symposium on Biomedical Imaging accepted. [BibTex]

2019

  • N. Gessert, M. Gromniak, M. Bengs, L. Matthäus, A. Schlaefer (2019). Towards Deep Learning-Based EEG Electrode Detection Using Automatically Generated Labels. CURAC 2019 Tagungsband Reutlingen 176-180. [Abstract] [www] [BibTex]

  • M. Bengs*, N. Gessert*, A. Schlaefer (2019). 4D Spatio-Temporal Deep Learning with 4D fMRI Data for Autism Spectrum Disorder Classification. International Conference on Medical Imaging with Deep Learning. Accepted. [openReview] *Authors contributed equally

  • N. Gessert*, M. Bengs*, L. Wittig, D. Drömann, T. Keck, A. Schlaefer, D. B. Ellebrecht (2019). Deep Transfer Learning Methods for Colon Cancer Classification in Confocal Laser Microscopy Images. International Journal of Computer Assisted Radiology and Surgery. [arxiv][doi]

Student Theses

In general, I supervise student theses with topics on deep learning for medical problems. In case you are interested in this field, contact me via E-Mail. 

Requirements:

  • Very good academic record
  • Very good programming skills (e.g. Python/Java/C/C++)
  • Background knowledge on machine learning and deep learning
  • Motivation and the ability to think critically and work independently

Please provide in your E-Mail:

  • Bachelor certificate and current transcript of records
  • Curriculum Vitae
  • A short text where you highlight your technical interests and related prior experiences