In his master's thesis, written in cooperation with HNU, the former student at Aalen University provides practical approaches to sustainably improve healthcare through IT: Daniel Hieber implemented a Convolutional Neural Network, an artificial neural network that classifies the heterogeneity of glioblastomas - malignant brain tumors - based on biopsies. This will enable rapid and automated heterogeneity determination, replacing the previous time-consuming and resource-intensive manual analysis by neuropathologists. The project will be put into practice at Augsburg University Hospital later this year to improve the analysis of glioblastomas in medical practice.
In his five-minute nomination video (see below - online at https://www.youtube.com/watch?v=VCgMnPmzCb0&ab_channel=DMEA), the HNU junior scientist explains the background to his work. Votes for the audience award can be cast at www.surveymonkey.de/r/AudienceAwardDMEA2023.
Daniel Hieber continues his previous work in a cooperative PhD with Prof. Dr. Rüdiger Pryss (University of Würzburg) and Prof. Dr. Johannes Schobel (Research Professor in the field of Digital Medicine and Nursing at HNU) under the title "Neural Network Assisted Pathology".
