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Invited Talk: Prof. Dr. Julia Schnabel – Smart Medical Imaging – from Sensors to Information, March 17th 2021, 15h CET

Prof. Dr. Schnabel has been shaping the world of medical imaging like few other persons. It’s great pleasure to have her here in Erlangen for an invited talk!

Title: Smart Medical Imaging – from sensors to information
Date: March 17th, 15h CET
Location: https://fau.zoom.us/j/94670845563?pwd=dmFsTDg1cFJQeWkweHBzbm92MzI3UT09

Abstract:  Medical imaging spans the entire process from acquisition, reconstruction, and quality control to image segmentation, classification, and interpretation. Recent years have increasingly seen the use of machine learning and deep learning architectures along the entire imaging pipeline, providing innovative end-to-end learning solutions that can operate directly on the imaging sensor during image acquisition, for online interpretation by the clinician.  In this talk I will focus on some recently developed “smart” medical imaging approaches applied to imaging problems in three major healthcare challenges: cancer, cardiovascular disease, and premature birth. I will specifically focus on physically and biologically realistic data augmentation, as well as real-time applications of our methods during scan-time, showing promise in image interpretation tasks that are typically only performed further down-stream, but that can equally contribute to achieving better image quality and more robust extraction of clinically relevant information.

Short Bio: Julia Schnabel graduated with an MSc in Computer Science at Technical University of Berlin (1993) and a PhD in Computer Science at University College London (1998), and subsequently held post-doctoral positions at University College London, King’s College London and University Medical Center Utrecht, before becoming first Associate Professor (2007) and then Full Professor (2014) of Engineering Science at the University of Oxford. In 2015 she joined King’s College London as Chair in Computational Imaging. Julia’s research focusses on machine/deep learning, complex motion modelling, as well as multi-modality and quantitative imaging for a range of medical imaging applications. She is serving on the Editorial Board of Medical Image Analysis, is Associate Editor for IEEE Transactions on Medical Imaging and  IEEE Transactions on Biomedical Engineering, and has recently founded the new free open-access Journal of Machine Learning for Biomedical Imaging (melba-journal.org). She has been Program Chair of  the MICCAI 2018 conference, is General Chair of IPMI 2021, and will be General Chair of MICCAI 2024, to be held for the first time in Africa. She is elected member of the IEEE EMBS Administrative Committee and the MICCAI Society Board of Directors, and an elected Fellow of the MICCAI Society (2018), ELLIS (2019), and IEEE (2021).