Gerd Heilemann, PhD
Department of Radiation Oncology
Position: Research Associate (Postdoc)
ORCID: 0000-0002-7461-3956
T +43 1 40400 72710
Keywords
Artificial Intelligence; Medical Physics; Radiation Oncology
Research group(s)
- Medical Radiation Research
Head: Dietmar Georg
Research Area: The vision of our group is the optimization of the treatment outcome of radiation oncology, alone or in combination with established chemotherapy or novel targeted strategies of drug treatment, with conventional photon or innovative ion-beams.
Members: - AI Research in Automated Radiation Oncology
Head: Gerd Heilemann
Research Area: We merge medical physics, data science, and artificial intelligence to advance radiation oncology. Our primary focus is the development of AI-based treatment planning models aimed at enhancing radiation therapy. Through interdisciplinary research and innovative computational methods, we strive to improve patient outcomes and contribute to the future of cancer treatment.
Members:
Grants
- Autonomous Radiotherapy Planning (2024)
Source of Funding: FWF (Austrian Science Fund), Principal Investigator Projects
Principal Investigator
Selected publications
- Heilemann, G. et al. (2023) ‘Generating deliverable DICOM RT treatment plans for prostate VMAT by predicting MLC motion sequences with an encoder‐decoder network’, Medical Physics [Preprint]. Available at: http://dx.doi.org/10.1002/mp.16545.
- Heilemann, G. et al. (2023) ‘Clinical Implementation and Evaluation of Auto-Segmentation Tools for Multi-Site Contouring in Radiotherapy’, Physics and Imaging in Radiation Oncology, 28, p. 100515. Available at: https://doi.org/10.1016/j.phro.2023.100515.
- Heilemann, G. et al. (2024) ‘Automation of ePROMs in radiation oncology and its impact on patient response and bias’, Radiotherapy and Oncology, 199, p. 110427. Available at: https://doi.org/10.1016/j.radonc.2024.110427.
- Heilemann, G. et al. (2023) ‘Increasing Quality and Efficiency of the Radiotherapy Treatment Planning Process by Constructing and Implementing a Workflow-Monitoring Application’, JCO Clinical Cancer Informatics [Preprint], (7). Available at: http://dx.doi.org/10.1200/cci.23.00005.
- Heilemann, G. et al. (2022) ‘Can Generative Adversarial Networks help to overcome the limited data problem in segmentation?’, Zeitschrift für Medizinische Physik, 32(3), pp. 361–368. Available at: http://dx.doi.org/10.1016/j.zemedi.2021.11.006.