Asst.-Prof. Christian Baumgartner, PhD
CV
Christian Baumgartner is Assistant Professor for Health Data Science at the Faculty of Health Sciences. He completed his Bachelor's degree in Information Technology and Electrical Engineering and his Master's degree in Biomedical Engineering at ETH Zurich. In 2016, he completed his doctorate in Biomedical Engineering at King's College London. From 2017-2019, postdoctoral positions took him to Imperial College London and back to ETH Zurich. After a year in industry working on augmented reality applications at PTC Vuforia, in 2021, he took over as head of an independent research group at the University of Tübingen. His research focuses on applications of artificial intelligence and data science in medicine. To ensure the safe integration of these technologies into everyday clinical practice, he designs customised machine learning algorithms that are specifically tailored to tackle specific medical challenges.
Publications
- Distribution shift detection for the postmarket surveillance of medical AI algorithms: a retrospective simulation study. (2024). npj Digital Medicine. https://doi.org/10.1038/s41746-024-01085-w
- Wirth, W., Maschek, S., Wisser, A., Eder, J., Baumgartner, C. F., Chaudhari, A., … Eckstein, F. (2024). Evaluation of an automated laminar cartilage T2 relaxation time analysis method in an early osteoarthritis model. Skeletal Radiology.
- Eberhardt, T., Schneider, M., Fischer, P., Gani, C., Baumgartner, C., & Thorwarth, D. (2024). SC29. 02 UNCERTAINTY ESTIMATION FOR AI-BASED DOSE MODELLING IN MR-GUIDED RADIOTHERAPY USING ENSEMBLE LEARNING AND MONTE CARLO DROPOUT. Physica Medica, 125, 103527 ff.
- Langner, D., Gani, C., Baumgartner, C., & Thorwarth, D. (2024). SC29. 03 AUTOMATIC AI-BASED SEGMENTATION OF LIVER METASTASES AND ORGANS-AT-RISK FOR MR-GUIDED RADIOTHERAPY. Physica Medica, 125, 103528 ff.
- Geuss, S., Jungmeister, A., Baumgart, A., Seelos, R., & Ockert, S. (2017). Fallbegleitende DRG Kodierung: Verbesserung von Wirtschaftlichkeit und Dokumentationsqualität in der stationären Versorgung. Chirurg, (11), 1–7. https://doi.org/https://doi.org/10.1007/s00104-017-0555-4
- Wundram, A. M., Fischer, P., Mühlebach, M., Koch, L. M., & Baumgartner, C. F. (2024). Conformal Performance Range Prediction for Segmentation Output Quality Control. In Sudre, Carole H., Mehta, Raghav, Ouyang, Cheng, Qin, Chen, Rakic, Marianne & Wells, William M. (Eds.), Uncertainty for Safe Utilization of Machine Learning in Medical Imaging: Springer. Cham, Swizterland: Springer.
- Woerner, S., & Baumgartner, C. F. (2024). Navigating Data Scarcity Using Foundation Models: A Benchmark of Few-Shot and Zero-Shot Learning Approaches in Medical Imaging. In Deng, Zhongying, Shen, Yiqing, Kim, Hyunwoo J., Jeong, Won-Ki, Aviles-Rivero, Angelica I., He, Junjun & Zhang, Shaoting (Eds.), Foundation Models for General Medical AI: Springer. Cham, Swizterland: Springer.
- Siegert, L., Fischer, P., Heinrich, M. P., & Baumgartner, C. F. (2024). PULPo: Probabilistic Unsupervised Laplacian Pyramid Registration. In Linguraru, Marius George, Dou, Qi, Feragen, Aasa, Giannarou, Stamatia, Glocker, Ben, Lekadir, Karim & Schnabel, Julia A. (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2024: Springer. Cham, Swizterland: Springer.
- Morshuis, J. N., Hein, M., & Baumgartner, C. F. (2024). Segmentation-guided MRI reconstruction for meaningfully diverse reconstructions. In Mukhopadhyay, Anirban, Oksuz, Ilkay, Engelhardt, Sandy, Mehrof, Dorit & Yuan, Yixuan (Eds.), Deep Generative Models: Springer. Cham, Swizterland: Springer.
- Donteu, K. R. D., Ilanchezian, I., Kühlewein, L., Faber, H., Baumgartner, C. F., Bah, B., … Koch, L. M. (2024). Sparse Activations for Interpretable Disease Grading. In Proceedings of Machine Learning Research (Ed.), Medical Imaging with Deep Learning. United States: Proceedings of Machine Learning Research.
