Research Output per year
Research Output per year
Personal profile
Scientific Focus
Research interests
- AI and Deep Learning
- Biomedical signal and image analysis
Current Project
PASBADIA (Patientennahe Smartphone-basierte Diagnostik mit lokaler und zentraler KI-Plattform für die Primärversorgung im ländlichen Raum)
Curriculum Vitae
Marlin Siebert holds a B.Sc. degree in Medical Engineering Science from Universität zu Lübeck, since 2017. His bachelor’s thesis contributed to the active power regulation of treatment lasers for the temperature-controlled photocoagulation of the retina. Completing his master thesis on the optimization and robustification of the model predictive control to the ventilation system of an anaesthesia workstation in 2019, he received the M.Sc. degree also in Medical Engieering Science at the University of Lübeck two years later. In 2018 he was an intern at Drägerwerk AG & Co. KGaA in the department for research and development and worked on medical deep learning.
Since 2020 he is employed as research associate at the Institute for Electrical Engineering in Medicine at the Universität zu Lübeck.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
External positions
Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering IMTE
01.05.2022 → 31.12.2023
Research Areas and Centers
- Academic Focus: Biomedical Engineering
- Research Area: Intelligent Systems
- Centers: Center for Artificial Intelligence Luebeck (ZKIL)
- Centers: Center for Open Innovation in Connected Health (COPICOH)
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Collaborations and top research areas from the last five years
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Research output
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Signal quality evaluation of single-channel respiratory sEMG recordings
Sauer, J., Siebert, M., Boudnik, L., Carbon, N. M., Walterspacher, S. & Rostalski, P., 01.2024, In: Biomedical Signal Processing and Control. 87, Part B, p. 105414 1 p., Part B.Research output: Journal Articles › Journal articles › Research › peer-review
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Uncertainty Analysis of Deep Kernel Learning Methods on Diabetic Retinopathy Grading
Siebert, M., Grasshoff, J. & Rostalski, P., 2023, In: IEEE Access. 11, p. 146173-146184 12 p.Research output: Journal Articles › Journal articles › Research › peer-review
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Performance evaluation of lightweight convolutional neural networks on retinal lesion segmentation
Siebert, M. & Rostalski, P., 2022, Medical Imaging 2022: Computer-Aided Diagnosis. Iftekharuddin, K. M., Drukker, K., Mazurowski, M. A., Lu, H., Muramatsu, C. & Samala, R. K. (eds.). SPIEResearch output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review
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Stochastic variational deep kernel learning based diabetic retinopathy severity grading
Siebert, M., Tesmer, N. & Rostalski, P., 01.08.2022, In: Current Directions in Biomedical Engineering. 8, 2, p. 408-411 4 p.Research output: Journal Articles › Journal articles › Research › peer-review
1 Citation (Scopus) -
Model predictive control of an anesthia workstation ventilation unit
Männel, G., Siebert, M., Kleinewalter, D., Brendle, C. & Rostalski, P., 2020, In: At-Automatisierungstechnik.Research output: Journal Articles › Conference Articles in Journals › Research › peer-review