Machine Learning
Machine learning with its capability to recognize complex patterns in big data is currently transforming cancer research. Many groups at DKFZ employ machine learning and deep learning to advance cancer research and treatment. Application areas include imaging and diagnostics, the analysis of high-dimensional omics datasets, the linking of clinical, experimental, imaging and omics data, and surgery and intervention. Methodologic research foci are causality, uncertainty, interpretability and scaling.
Bangert group
Our work revolves around the computational incorporation of physical and numerical models into the treatment optimization process for cancer therapy with radiation.
Proportion of this group's activities taken up by computational research: 80%
Floca group
Software development for Integrated Diagnostic and Therapy
Computational research in the context of uncertainty quantification and hyper parameter optimization of image processing pipelines as well as medical image registration.
Proportion of this group's activities taken up by computational research: 50%
Jäger Group
Machine learning research with a focus on human-machine-interaction and diverse imaging applications from all across Helmholtz.
Proportion of this group's activities taken up by computational research: 100%
Klaus Maier-Hein group
The group develops machine learning algorithms, mathematical modelling approaches and the required research software infrastructure for computational image understanding and large-scale information processing.
Proportion of this group's activities taken up by computational research: 100%
Lena Maier-Hein group
The vision of the division is to observe everything occurring within and around the surgical treatment process in order to provide context-aware assistance to the physicians.
Proportion of this group's activities taken up by computational research: 100%
Stegle group
Computational Genomics & Systems Genetics
The Stegle team uses statistics and machine learning to analyse and integrate genomic variation datasets.
Proportion of this group's activities taken up by computational research: 100%