Implicit Neural Representations for Medical Imaging

Julian McGinnis is a PhD student at the Department of Neurology and the Institute of Artificial Intelligence in Medicine at the Technical University of Munich (TUM). His research is centered on the intersection of medicine, neuroscience, and machine learning, specifically focusing on Multiple Sclerosis (MS) and the integrated analysis of the brain and spinal cord. His interests revolve around implicit neural representations and compact latent representations for various downstream tasks. These tasks include generative modeling, image reconstruction, and lesion segmentation.

Nil Stolt-Ansó is a PhD student at the chair for Artificial Intelligence in Medicine and a member of the Munich Center for Machine Learning (MCML). He studied Artificial Intelligence with a focus on Machine Learning at the University of Amsterdam (UvA), receiving his M.Sc. in 2020. Prior to starting his PhD, Nil worked in the medical imaging industry, focusing on topics such as blood vessel segmentation, myocardial tissue tracking, and cardiac phase modelling. His current interests concern the application of geometric deep learning to imaging tasks.

Veronika Spieker is a PhD student at the Institute of Machine Learning for Biomedical Imaging (IML) at Helmholtz Munich and Technical University of Munich (TUM). After completing her B.Sc. in engineering at TU Darmstadt and Virginia Tech, she pursued her interest in the medical domain with a M.Sc. in Medical Technologies at TUM. For her PhD project, she works on Physics-Based AI for Motion Correction in Abdominal MRI in collaboration with the Body Magnetic Resonance Group at the Klinikum rechts der Isar. Her research interests include concepts such as neural implicit representations and it’s application to MR reconstruction and motion estimation.

I am currently a postdoctoral researcher at the University of Zürich (UZH) and an associate researcher at the ETH AI Center. My primary research focuses on medical imaging applications using neural implicit representation. I am also interested in extracting efficient and meaningful representations from the image domain for topology and graph domain processing.

Maik Dannecker is a PhD student at the Artificial Intelligence and Informatics in Medicine at the Technical University of Munich (TUM). He received his Bachelor’s and Master’s degrees in Informatics from TUM. In this Master’s thesis he compared classic and deep learning methods for deformable image registration in CT scans of the lungs. In his PhD he is developing methods for the quantification and prediction of abnormal growth of the brain in fetal MRI.

Vasiliki Sideri-Lampretsa is a Ph.D. student at the Institute for Artificial Intelligence and Informatics in Medicine at the Technical University of Munich (TUM). She received her Diploma in Electrical and Computer Engineering at Aristotle University of Thessaloniki and her Master’s degree in Computer Science at TUM. She was a research assistant at the Interdisciplinary Research Lab (IFL) at TUM, where she also conducted her Master’s thesis. During her Master’s thesis, she focused on employing deep learning to tackle the inverse problem of predicting the speed of sound using ultrasound raw channel data aquired from simulations. Currently, her research interest lies in the field of machine learning in medical imaging, particularly in the fields of feature representation and unsupervised learning.