Cookie Settings

We use cookies to optimize our website. These include cookies that are necessary for the operation of the site, as well as those that are only used for anonymous statistic. You can decide for yourself which categories you want to allow. Further information can be found in our data privacy protection .

Essential

These cookies are necessary to run the core functionalities of this website and cannot be disabled.

Name Webedition CMS
Purpose This cookie is required by the CMS (Content Management System) Webedition for the system to function correctly. Typically, this cookie is deleted when the browser is closed.
Name econda
Purpose Session cookie emos_jcsid for the web analysis software econda. This runs in the “anonymized measurement” mode. There is no personal reference. As soon as the user leaves the site, tracking is ended and all data in the browser are automatically deleted.
Statistics

These cookies help us understand how visitors interact with our website by collecting and analyzing information anonymously. Depending on the tool, one or more cookies are set by the provider.

Name econda
Purpose Statistics
External media

Content from external media platforms is blocked by default. If cookies from external media are accepted, access to this content no longer requires manual consent.

Name YouTube
Purpose Show YouTube content
Name Twitter
Purpose activate Twitter Feeds

Division of Intelligent Medical Systems

Prof. Dr. Lena Maier-Hein

© dkfz.de

The mission of the Div. Intelligent Medical Systems is to improve the quality of interventional healthcare in a data-driven manner. To this end, our multidisciplinary group builds upon principles and knowledge from a diversity of research fields including artificial intelligence (AI), statistics, computer vision, biophotonics and medicine. Committed to the ultimate goal of creating benefit for patients and medical staff, we aim to develop a holistic concept spanning the three significant topics perception, data interpretation and real-time assistance and connecting them through a cycle of continuous learning: Novel spectral imaging techniques enabled by deep learning are being developed as safe, reliable and real-time imaging modalities during interventions. When interpreting the perceived data in the context of available knowledge, our division specifically addresses common roadblocks to clinical translation such as data sparsity, explainability and uncertainty handling. In close collaboration with clinical partners, these methods are leveraged for the development of context-aware interventional assistance systems. Finally, we place a strong focus on the reliable validation of AI algorithms for clinical purposes.

Contact

Prof. Dr. Lena Maier-Hein
Intelligent Medical Systems (E130)
Deutsches Krebsforschungszentrum
Im Neuenheimer Feld 223
69120 Heidelberg
Tel: +49 6221 42 2354

Selected Publications

  • Ayala L, Adler T et al., Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery. Science Advances, in print (2023).
  • Maier-Hein L, et al. Surgical data science - from concepts toward clinical translation. Medical Image Analysis 76:102306 (2022)
  • Gröhl J, et al. Learned spectral decoloring enables photoacoustic oximetry. Scientific Reports 11:6565 (2021)
  • Ardizzone L, et al. Analyzing inverse problems with invertible neural networks. International Conference on Learning Representations (ICLR) 2019
to top
powered by webEdition CMS