Dr. Martin Menten
Emmy Noether Junior Research Group
Harnessing Multimodal Learning Signals to Advance Ophthalmological Imaging
Chair of Artificial Intelligence in Healthcare and Medicine
Academic Career and Research Areas
Dr. Menten specializes in machine learning for medical imaging. His research focuses on weakly and self-supervised learning to address data scarcity in healthcare and the integration of multimodal clinical data with medical images. He is particularly interested in the development and application of machine learning and computer vision algorithms in the field of ophthalmology.
He studied physics at the University of Heidelberg from 2008 to 2014. Subsequently, he earned his PhD from the Institute of Cancer Research in London, where he developed automated strategies to adapt external beam radiotherapy for cancer to anatomical changes. After completing his doctoral studies in 2019, he transitioned to computer science by joining the Biomedical Image Analysis group at Imperial College London. In 2021, he moved to the Technical University of Munich, where he established his own independent research group in 2024.
Key Publications (all publications)
Kreitner L, Paetzold JC, Rauch N, Chen C, Hagag AM, Fayed AE, Sivaprasad S, Rausch S, Weichsel J, Menze BH, Harders M, Knier B, Rueckert D, Menten MJ: "Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations." IEEE Transactions on Medical Imaging. 2024
AbstractMenten MJ, Paetzold JC, Zimmer VA, Shit S, Ezhov I, Holland R, Probst M, Schnabel JA, Rueckert D: "A Skeletonization Algorithm for Gradient-Based Optimization." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023
AbstractHolland R, Leingang O, Holmes C, Anders P, Kaye R, Riedl S, Paetzold JC, Ezhov I, Bogunović H, Schmidt-Erfurth U, Scholl HPN, Sivaprasad S, Lotery AJ, Rueckert D, Menten MJ: "Clustering Disease Trajectories in Contrastive Feature Space for Biomarker Proposal in Age-Related Macular Degeneration." International Conference on Medical Image Computing and Computer-Assisted Intervention. 2023
AbstractHager P, Menten MJ, Rueckert D: "Best of both worlds: multimodal contrastive learning with tabular and imaging data." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023
AbstractMenten MJ, Paetzold JC, Dima A, Menze BH, Knier B, Rueckert D: "Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs." International Conference on Medical Image Computing and Computer-Assisted Intervention. 2022
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