Asst.-Prof. Christian Baumgartner, PhD
CV
Christian Baumgartner is Assistant Professor of 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.
Projects
No results found.Publications
- Suggs, S., Stefano, W., Andreas, M., Koch, L., & Baumgartner, C. F. (2025). Attri-Net: A Globally and Locally Inherently Interpretable Model for Multi-Label Classification Using Class-Specific Counterfactuals. Machine Learning for Biomedical Imaging. https://doi.org/10.59275/j.melba.2025-gb33
- Schneider, M., Simon, G., David, M., Gani, C., Fischer, P., Baumgartner, C. F., & Thorwarth, D. (2025). Development and comprehensive clinical validation of a deep neural network for radiation dose modelling to enhance magnetic resonance imaging guided radiotherapy. Physics and Imaging in Radiation Oncology. https://doi.org/10.1016/j.phro.2025.100723
- Schneider, M., Eberhardt, T., Fischer, P., Thorwarth, D., Niyazi, M., & Baumgartner, C. F. (2025). Estimating uncertainty for AI-based dose modelling in MRI-guided RT using ensemble networks, mean variance estimation and Monte Carlo dropout. Radiotherapy and Oncology. https://doi.org/10.1016/s0167-8140(25)00742-x
- Wirth, W., Maschek, S., Wisser, A., Eder, J., Baumgartner, C. F., Chaudhari, A., … on, C. (2025). Evaluation of an automated laminar cartilage T2 relaxation time analysis method in an early osteoarthritis model. Skeletal Radiology. https://doi.org/10.1007/s00256-024-04786-1
- Martin, P., Christian, S., & Baumgartner, C. F. (2025). Studying therapy effects and disease outcomes in silico using artificial counterfactual tissue samples. Computers in Biology and Medicine. https://doi.org/10.1016/j.compbiomed.2025.110997
- Alexander, F., Jaivardhan, K., Thomas, W., & Baumgartner, C. F. (2025). Unsupervised anomaly detection in medical imaging using aggregated normative diffusion. Medical Image Analysis. https://doi.org/10.1016/j.media.2025.103895
- Koch, L., Baumgartner, C. F., & Borens, P. (2024). Distribution shift detection for the postmarket surveillance of medical AI algorithms: a retrospective simulation study. 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.
- Corik, I., Langner, D., Gani, C., Thorwarth, D., & Baumgartner, C. F. (2024). SC29. 03 Automatic AI-based Segmentation of Liver Metastases and Orgams-at-Risk for MR-Guided Radiotherapy. Physica Medica, 125, 103528 ff.
- Gani, C., Thorwarth, D., Schneider, M., Eberhardt, T., Baumgartner, C. F., & Fischer, P. (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.
- Jungmeister, A., Geuss, S., Baumgartner, C. F., 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
- Fuchs, M., Angelopoulos, A. N., Paschali, M., Baumgartner, C. F., & Mukhopadhy, A. (2025). Navigating the unknown: out-of-distribution detection for medical imaging. In Lorenzi, Marcol & Zuluaga, Maria (Eds.), Trustworthy AI in Medical Imaging. Cambridge, Massachusetts, United States: Academic Press. https://doi.org/10.1016/b978-0-44-323761-4.00013-4
- Sun, S., van Midden, D., Litjens, G., & Baumgartner, C. F. (2025). Prototype-Based Multiple Instance Learning for Gigapixel Whole Slide Image Classification. In Gee, James, Alexander, Daniel, Hong, Jaesung, Iglesias, Juan Eugenio, Sudre, Carole, Venkataraman, Archana, … Park, Jinah (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2025: Lecture Notes in Computer Science (LNCS, volume 15973). Cham, Switzerland: Springer Nature. https://doi.org/10.1007/978-3-032-05185-1_49
- Baumgartner, C. F., & Wundram, A. M. (2025). Is Uncertainty Quantification a Viable Alternative to Learned Deferral? In Sudre, Carole H. and Hoque, Mobarak I. and Mehta, Raghav and Ouyang, Cheng and Qin, Chen and Rakic, Marianne and Wells, William M. (Ed.), Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (pp. 34-44 ff.). Cham: Springer Nature Switzerland.
- Christian, S., Thomas, K., Baumgartner, C. F., & Hein, M. (2025). Mind the Detail: Uncovering Clinically Relevant Image Details in Accelerated MRI with Semantically Diverse Reconstructions. In Gee, James, Alexander, Daniel, Hong, Jaesung, Iglesias, Juan Eugenio, Sudre, Carole, Venkataraman, Archana, … Park, Jinah (Eds.), Medical Image Computing and Computer-Assisted Interventions: Medical Image Computing and Computer-Assisted Interventions. Cham, Switzerland: Spring Nature. https://doi.org/10.1007/978-3-032-04937-7_34
- Alceu, B., Trung-Dung, H., Tim, F., Suggs, S., Baumgartner, C. F., & Koch, L. (2025). Subgroup Performance Analysis in Hidden Stratifications. In Gee, James, Alexander, Daniel, Hong, Jaesung, Iglesias, Juan Eugenio, Sudre, Carole, Venkataraman, Archana, … Park, Jinah (Eds.), Medical Image Computing and Computer-Assisted Interventions: Medical Image Computing and Computer Assisted Interventions (MICCAI). Cham, Switzerland: Spring Nature. https://doi.org/10.1007/978-3-032-05185-1_57
- Wundram, A., Fischer, P., Mühlebach, M., Koch, L., & 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.
- Stefano, W., & 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., & 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, 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 Djoumessi, R., Ilanchezian, I., Kühlewein, L., Faber, H., Baumgartner, C. F., Bah, B., … Koch, L. (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.
