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!
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).
Oksuz I, Ruijsink JB, Puyol Anton E, Clough JR, Lima da 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). 10.1016/j.media.2019.04.009
Oksuz 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). 10.1109/TMI.2020.3008930
Ruijsink B, Puyol-Antón E, Oksuz I, Sinclair M, Bai W, Schnabel JA, Razavi R, King AP. Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function. JACC: Cardiovascular Imaging (2019). 10.1016/j.jcmg.2019.05.030
Martinez O, Ellis S, Baltatzis V, Devaraj A, Desai S, Le Golgoc, Nair A, Glocker B, Schnabel JA. Data Augmentation for Early Stage Lung Nodules using Deep Image Prior and CycleGan. In: MED-NEURIPS (2019).
Martinez O, Ellis S, Baltatzis V, Nair A, Le Folgoc L, Desai S, Glocker B, Schnabel JA. Patient-Specific 3D Cellular Automata Nodule Growth Synthesis in Lung Cancer without the Need of External Data. Accepted for IEEE Symposium on Biomedical Imaging - ISBI 2021.
Zimmer VA, Gómez A, Skelton E, Toussaint N, Zhang T, Khanal B, Wright R, Noh Y, Ho A, Matthew J, Hajnal JV, Schnabel JA. Towards Whole Placenta Segmentation at Late Gestation Using Multi-view Ultrasound Images. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2019, Lecture Notes in Computer Science, vol 11768, Springer (2019) https://doi.org/10.1007/978-3-030-32254-0_70
Zimmer VA, Gómez A, Skelton E, Ghavami N, Wright R, Li L, Matthew J, Hajnal JV, Schnabel JA. A Multi-task Approach Using Positional Information for Ultrasound Placenta Segmentation. In: Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis. ASMUS 2020, PIPPI 2020. Lecture Notes in Computer Science, vol 12437, Springer (2020) https://doi.org/10.1007/978-3-030-60334-2_26
Wright R, Toussaint N, Gómez A, Zimmer VA, Khanal B, Matthew J, Skelton E, Kainz B, Rueckert D, Hajnal JV, Schnabel JA. Complete Fetal Head Compounding from Multi-view 3D Ultrasound. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2019, Lecture Notes in Computer Science, vol 11766, Springer (2019) https://doi.org/10.1007/978-3-030-32248-9_43
Toussaint N, Khanal B, Sinclair M, Gomez A, Skelton E, Matthew J, Schnabel JA. Weakly Supervised Localisation for Fetal Ultrasound Images. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. DLMIA 2018, ML-CDS 2018. Lecture Notes in Computer Science, vol 11045, Springer (2018). 10.1007/978-3-030-00889-5_22
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