Prof. Dr. Julia Schnabel
Academic Career and Research Areas
Prof Schnabel's (*1969) field of research comprises medical image computing and machine learning. Her research focuses on intelligent imaging solutions and computer aided evaluation, including complex motion modelling, image reconstruction, image quality control, image segmentation and classification, applied to multi-modal, quantitative and dynamic imaging.
Since 2021 Julia Schnabel is Professor for Computational Imaging and AI in Medicine at TUM (TUM Liesel Beckmann Distinguished Professorship), jointly with Helmholtz Center Munich (Helmholtz Distinguished Professorship). She studied at TU Berlin (1993) and did a PhD at University College London (1998), followed by Postdocs at UMC Utrecht, King's College London, and UCL. In 2007 she became first Associate Professor and in 2014 Full Professor of Engineering Science at University of Oxford, and from 2015 Chair in Computational Imaging at King's College.
Awards
- TUM Liesel Beckmann Distinguished Professorship (2021)
- Helmholtz Distinguished Professorship (2021)
- Fellow, IEEE (2021)
- Fellow, ELLIS (2019)
- Fellow, MICCAI Society (2018)
- Erwin Stephan Prize (1998)
Key Publications (all publications)
Clough J, Byrne N, Öksüz I, Zimmer VA, Schnabel JA, King A: “A Topological Loss Function for Deep-learning based image segmentation using persistent homology”. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2020.
AbstractRueckert D, Schnabel JA. “Model-based and data-driven strategies in medical image computing”. Proceedings of the IEEE. 2019; 108(10):110-124.
AbstractÖksüz I, Clough J, Ruijsink B, Puyol Antón E, Bustin A, Cruz G, Prieto C, King AP, Schnabel JA. “Deep learning-based detection and correction of cardiac MR motion artefacts during reconstruction for high-quality segmentation”. IEEE Transactions on Medical Imaging. 2020; 39(12):4001-4010.
AbstractÖksüz, Ruijsink JB, Puyol Anton E, Clough JR, Limada Cruz, GJ, Bustin A, Prieto Vasquez C, Botnar RM, Rueckert D, Schnabel JA, King AP. “Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning”. Medical Image Analysis. 2019; 55:136-147
AbstractSchnabel JA, Heinrich MP, Papież BW, Brady Sir JM. “Advances and challenges in deformable image registration: From image fusion to complex motion modelling”. Medical Image Analysis. 2016; 33:145-148.
Abstract