Eindhoven University of Technology
Friso Heslinga Eindhoven University of Technology f.g.heslinga@tue.nl

PhD Candidate
E-mail: f.g.heslinga@tue.nl
Phone: +31 40 24 75581
LinkedIn; Google Scholar


Friso Gerben Heslinga is a PhD candidate at the medical image analysis group (IMAG/e) at the Eindhoven University of Technology. His PhD research is on deep learning for weakly labeled data and focuses on retina images. Deep learning is used to support physicians with the diagnoses and treatment of both eye-specific and systematic diseases by (1) the construction of predictive models and (2) the discovery of biomarkers and structured feedback mechanisms. We aim to develop generalizable models to improve scientific knowledge and the care of patients.

Friso obtained a BSc. and a MSc. in biomedical engineering at the University of Twente. He specialized in biomedical physics and medical imaging modalities at the Magnetic Detection & Imaging group. Friso worked on the development of a phantom for MRI-research during an internship at the BioMagnetics group at the University of Western Australia. His master thesis project took place in the Conolly lab at the University of Califonia, Berkeley, where he compared MRI with Magnetic Particle Imaging (MPI) for stem cell tracking potential. Friso also obtained a MSc. in Health Sciences (cum laude) at the University of Twente where he focused on techniques for (early) health technology assessment. Friso applied these techniques to innovative technologies for the hybrid operating theater at the Health Technology and Services Research group, and set up a project for the acceptance of robotic guidance for prostate surgery at the Surgical Planning Lab in Brigham and Women’s hospital, Boston.

Friso’s research interests include biomedical physics, medical image analysis, deep learning and health economics. However, he is also interested in discussing various other topics, so feel free to contact him with any questions or discussions!


Friso G. Heslinga, Mark Alberti, Josien P.W. Pluim, Javier Cabrerizo, Mitko Veta

Quantifying Graft Detachment after Descemet's Membrane Endothelial Keratoplasty with Deep Convolutional Neural Networks Journal Article Forthcoming

Translational Vision Science & Technology, Forthcoming.

Abstract | BibTeX


F.G. Heslinga, J.P.W. Pluim, A.J. Houben, M.T. Schram, R.M. Henry, C.D. Stehouwer, M.J. Van Greevenbroek, T.T. Berendschot, M. Veta

Direct Classification of Type 2 Diabetes From Retinal Fundus Images in a Population-based Sample From The Maastricht Study Inproceedings

Medical Imaging 2020: Computer-Aided Diagnosis, pp. 113141N, International Society for Optics and Photonics, 2020.

Abstract | Links | BibTeX


Friso G. Heslinga, Josien P. W. Pluim, Behdad Dashtbozorg, Tos T. J. M. Berendschot, A. J. H. M. Houben, Ronald M. A. Henry, Mitko Veta

Approximation of a pipeline of unsupervised retina image analysis methods with a CNN Inproceedings

SPIE Medical Imaging, 2019.

Abstract | Links | BibTeX