Daniel Hieber

Medizin, Universität Augsburg, Neu-Ulm Universität für angewandte Wissenschaften

Kurzbeschreibung des Promotionsvorhabens 

Glioblastoma (GBM) is the most common malignant brain tumor with a poor overall survival of only 15 months and high resistance to therapy. This resistance is attributed to the high heterogeneity of the tumors, making individualized treatment of each tumor a priority.
However, there is limited research on the heterogeneity of GBMs due to the time-consuming and complex process of heterogeneity determination.
The aim of my PhD project is to enable heterogeneity assessment in routine diagnostics by automating the process using computer vision and machine learning, as a step towards personalized medicine. The approaches are based on histopathological data, which provide more detail than radiological data.