Marlin Siebert
Research Associate
Institute for Electrical Engineering in Medicine
Universität zu Lübeck
Moislinger Allee 53-55
23558 Lübeck
Gebäude 19
Email: | m.siebert(at)uni-luebeck.de |
Phone: | +49 451 3101 6220 |
Research
Research interests
- AI and Deep Learning
- Biomedical signal and image analysis
Current Projects
- 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.
Publications
Signal quality evaluation of single-channel respiratory sEMG recordings, Biomedical Signal Processing and Control , vol. 87, pp. 105414, 2024.
DOI: | https://doi.org/10.1016/j.bspc.2023.105414 |
Bibtex: | @article{Sauer2024, title = {Signal quality evaluation of single-channel respiratory sEMG recordings}, journal = {Biomedical Signal Processing and Control}, volume = {87}, pages = {105414}, year = {2024}, issn = {1746-8094}, doi = {https://doi.org/10.1016/j.bspc.2023.105414}, url = {https://www.sciencedirect.com/science/article/pii/S1746809423008479}, author = {Julia Sauer and Marlin Siebert and Lukas Boudnik and Niklas M. Carbon and Stephan Walterspacher and Philipp Rostalski}, keywords = {Surface electromyography, Quality evaluation, Signal-to-disturbance ratio, Respiration} } |
Uncertainty Analysis of Deep Kernel Learning Methods on Diabetic Retinopathy Grading, 2023.
Stochastic variational deep kernel learning based diabetic retinopathy severity grading, Current Directions in Biomedical Engineering , vol. 8, no. 2, pp. 408--411, 2022. Walter de Gruyter GmbH.
DOI: | 10.1515/cdbme-2022-1104 |
File: | 10.1515%2Fcdbme-2022-1104 |
Bibtex: | @article{Siebert_2022, doi = {10.1515/cdbme-2022-1104}, url = {https://doi.org/10.1515%2Fcdbme-2022-1104}, year = {2022}, month = {aug}, publisher = {Walter de Gruyter {GmbH}}, volume = {8}, number = {2}, pages = {408--411}, author = {Marlin Siebert and Nikolay Tesmer and Philipp Rostalski}, title = {Stochastic variational deep kernel learning based diabetic retinopathy severity grading}, journal = {Current Directions in Biomedical Engineering} } |
Performance evaluation of lightweight convolutional neural networks on retinal lesion segmentation, in Medical Imaging 2022: Computer-Aided Diagnosis , Karen Drukker and Khan M. Iftekharuddin and Hongbing Lu and Maciej A. Mazurowski and Chisako Muramatsu and Ravi K. Samala, Eds. SPIE, 2022. pp. 806-817.
DOI: | 10.1117/12.2611796 |
File: | SiRo22.pdf |
Bibtex: | @inproceedings{SiRo22, author = {M. Siebert and P. Rostalski}, title = {{Performance evaluation of lightweight convolutional neural networks on retinal lesion segmentation}}, volume = {12033}, booktitle = {Medical Imaging 2022: Computer-Aided Diagnosis}, editor = {Karen Drukker and Khan M. Iftekharuddin and Hongbing Lu and Maciej A. Mazurowski and Chisako Muramatsu and Ravi K. Samala}, organization = {International Society for Optics and Photonics}, publisher = {SPIE}, pages = {806 -- 817}, keywords = {diabetic retinopathy, deep learning , multi-lesion segmentation, U-Net, fundus image, mobile segmentation}, year = {2022}, doi = {10.1117/12.2611796}, URL = {https://doi.org/10.1117/12.2611796} } |
Robust Model Predictive Control of an Anaesthesia Workstation Ventilation Unit, at - Automatisierungstechnik , 2020.
Model Predictive Control of an Anaesthesia Workstation Ventilation Unit, in 21st IFAC World Congress , 2020.
Tube-based MPC for Pressure Controlled Ventilation, in Proceedings on Automation in Medical Engineering , 2020.
Fusing information from multiple 2D depth cameras for 3D human pose estimation in the operating room, International Journal of Computer Assisted Radiology and Surgery , vol. 14, no. 11, pp. 1871-1879, 2019. Springer.
Optimization and Robustification of the Model Predictive Control to the Ventilation System of an Anaesthesia Workstation, Master's Thesis, Universität zu Lübeck, 2019.
Temperature-Controlled Laser Therapy of the Retina via Robust Adaptive ℋ∞-Control, at - Automatisierungstechnik, Invited Article in Special Issue "AUTOMED 2018" , vol. 66, no. 12, pp. 1051--1063, 2018.
DOI: | 10.1515/auto-2018-0066 |
Bibtex: | @article{HeThScSiBr18, author = {Herzog, Christian and Thomsen, Ole and Schmarbeck, Benedikt and Siebert, Marlin and Brinkmann, Ralf}, year = {2018}, title = {{Temperature-Controlled Laser Therapy of the Retina via Robust Adaptive ℋ∞-Control}}, journal = {{at - Automatisierungstechnik, Invited Article in Special Issue "AUTOMED 2018"}}, doi = {10.1515/auto-2018-0066}, issn = {0178-2312}, number = {12}, pages = {1051--1063}, volume = {66}, year = {2018} } |
Leistungsgesteuerte temperaturüberwachte Lasertherapie der Netzhaut mittels adaptiv robuster ℋ∞-Regelung, in Proc. Workshop Automed , 2018.
Aktive Leistungsregelung des Behandlungslasers für die kontrollierte Photokoagulation der Netzhaut, Bachelor Thesis, Universität zu Lübeck, 2016.
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