During his PhD at the Division of Medical Image Computing of the German Cancer Research Center, Fabian Isensee has researched deep learning techniques for semantic segmentation of (three-dimensional) datasets in the biological and medical domain. He has been particularly active in the development of methods for automated segmentation pipeline design. His most prominent effort in that regard, nnU-Net, has become the de-facto standard for segmentation in the medical domain and was recently accepted for publication in Nature Methods. Throughout his PhD, Fabian Isensee has consistently enabled the translation of state-of-the-art algorithms into real-world applications. He is furthermore adamant about making his research publicly available. The methods he developed have won multiple international segmentation competitions.
Since 2020, Fabian Isensee is heading the HI Support Unit ‘Applied Computer Vision Lab’ at the DKFZ with the goal of translating state-of-the-art AI methods to the many diverse research applications found across the Helmholtz association. The support unit is working in close collaboration with Helmholtz researchers, provides consulting, develops domain agnostic state-of-the art software for AI-based image analysis and provides solutions for algorithm and competition evaluation.