Thomas de Bel
Thomas de Bel completed his masters in Data Science at the Radboud University in Nijmegen. For his thesis he worked on applying deep learning systems to analyse and segment stuctures of PAS-stained renal tissue in the Computational Pathology group. Starting in March 2018, he continues as a PhD-student in this group to work on deep learning based histological assessment of the stroma for improved risk stratification of ductal carcinoma in situ (DCIS) patients. This project is funded by the Junior Research Grant from the Radboud Institute for Health Sciences in 2018. He is supervised by Jeroen van der Laak and Geert Litjens.