M. Sc. Marten Finck
Contact
Room: EG.002
E-Mail: mafi@informatik.uni-kiel.de
Tel: +49 431-880-7096​​​​​​​​​​​
Social Media: LinkedIn
Research Interests
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Generative AI
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Medical AI
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Deep Learning
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Machine Learning
Publications
N. C. Koser, M. J. Finck, F. N. von Brackel, B. Ondruschka, S. Pirk, C.-C. Glüer, Real Super-Resolution
for Proximal Femur: Enhanced Computation of Structural Bone Metrics from Clinical CTs, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2025)
[Preprint], [Poster]
M. J. Finck, N. C. Koser, J. B. Hövener, C. C. Glüer, S. Prik, FemoraLyze: A Modular Framework for Proximal Femur Analysis, Medical Imaging with Deep Learning-Short Papers. 2025
L. Nolte, M. J. Finck, S. Pirk, S. Tomforde, Feedback-guided Dataset Shaping for Automated Downstream Task Optimization, GI/ITG International Conference on Architecture of Computing Systems (ARCS), 2025
[Preprint]
M. J. Finck, K. Krüger, S. Paschen, A.-K. Helmers, G. Schmidt, Analyzing Impaired Speech in Context of Magnetic Resonance-guided Focused Ultrasound Using Convolutional Neural Networks, DEGA e.V. (Hrsg.), Fortschritte der Akustik – DAGA 2024, 2024
[Macau]
K. Naderi Beni, R. Wolke, M. J. Finck, E. Elfrath, N. G. Margraf, R. Rieger, Acquisition and Automated Segmentation of Inertia Sensor Data for Mobile Camptocormia Assessment, 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland, United Kingdom, pp. 105-108, 2022
[IEEE]
Short CV
since January 2024
PhD Student and Research Assistant
Research group Visual Computing and Artificial Intelligence of the Institute of Computer Science at Kiel University.
2022 - 2023
M. Sc. Wirtschaftsingenieurwesen Elektrotechnik und Informationstechnik
Kiel University
Specialization in Machine Learning and medical technology
Thesis Title: Evaluation of Speech Quality using Machine Learning
2018 - 2022
B. Sc. Wirtschaftsingenieurwesen Elektrotechnik und Informationstechnik
Kiel University
Thesis Title: Acquisition and automated segmentation of IMU sensor data for camptocormia assessment