Contact information
Evan Hann
DPhil Student
Research
• Conduct research alongside clinician-scientists to improve clinical processes and patient outcomes
• Automate large-scale anonymised image data analysis for the UK Biobank Imaging Component (100,000 participants)
• Design novel data-efficient AI/machine learning quality control framework to safeguard automated analysis and to provide actionable insights for human-in-the-loop workflow
• Implement fully convolutional neural networks in Python, Tensorflow, Keras
• Attend and present at top clinical and technical conferences (SCMR, MICCAI, ISBI)
• Patent research output in collaboration with Oxford University Innovation
• Funded by Clarendon Fund Scholarship and Radcliffe Department of Medicine Scholar Programme
• Research and travel awards from BHF, SCMR, MICCAI, etc
Recent publications
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Quality control-driven framework for reliable automated segmentation of cardiac magnetic resonance LGE and VNE images
Conference paper
Gonzales RA. et al, (2023)
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Deep neural network ensemble for on-the-fly quality control-driven segmentation of cardiac MRI T1 mapping.
Journal article
Hann E. et al, (2021), Med Image Anal, 71
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Cardiac stress T1-mapping response and extracellular volume stability of MOLLI-based T1-mapping methods.
Journal article
Burrage MK. et al, (2021), Sci Rep, 11
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Total Mapping Toolbox (TOMATO): An open source library for cardiac magnetic resonance parametric mapping
Journal article
Werys K. et al, (2020), SoftwareX, 11
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Quality Control-Driven Image Segmentation Towards Reliable Automatic Image Analysis in Large-Scale Cardiovascular Magnetic Resonance Aortic Cine Imaging
Conference paper
Hann E. et al, (2019), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11765 LNCS, 750 - 758