My research focus is on developing robust deep learning approaches for accountable contrast-agent-free cardiac magnetic resonance (CMR) imaging in clinical applications. I design novel data-driven methods to automatically derive predictive biomarkers on clinical outcomes. My DPhil programme is funded by the Clarendon Fund Scholarship and Radcliffe Department of Medicine Scholar Programme.
Previously, I received my undergraduate degree in Electrical Engineering at UTEC (Peru) and my research training at Yale University (USA) and Lund University (Sweden), where I developed tools for the assessment of diastolic function in CMR, and its relationship to atrial remodeling. Outside of work, I serve as the Computer Science Head at REPU, a career progression program.
Automated left atrial time-resolved segmentation in MRI long-axis cine images using active contours
Gonzales RA. et al, (2021), BMC Medical Imaging, 21
Deep neural network ensemble for on-the-fly quality control-driven segmentation of cardiac MRI T1 mapping.
Hann E. et al, (2021), Med Image Anal, 71
Ensemble of Deep Convolutional Neural Networks with Monte Carlo Dropout Sampling for Automated Image Segmentation Quality Control and Robust Deep Learning Using Small Datasets
Hann E. et al, (2021), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12722 LNCS, 280 - 293
TVnet: Automated Time-Resolved Tracking of the Tricuspid Valve Plane in MRI Long-Axis Cine Images with a Dual-Stage Deep Learning Pipeline
Gonzales RA. et al, (2021), Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 567 - 576
Valvular imaging in the era of feature-tracking: A slice-following cardiac MR sequence to measure mitral flow
Seemann F. et al, (2020), Journal of Magnetic Resonance Imaging, 51, 1412 - 1421