Mark Pinnock Email & Phone Number

London, Gb

London, England, United Kingdom

London, England, United Kingdom

- Translational research for collaboration between UCLH and Microsoft Research Cambridge for detecting abnormally placed nasogastric tubes on chest x-rays- Refined deep learning models on challenging clinical data within Azure TRE- Explored the use of explainability and uncertainty estimation- Handled ethical review process and study protocols

London, United Kingdom

Thesis: "Super-Resolution and Contrast Enhancement in Interventional Computed Tomography"- Performed super-resolution work to improve image quality in interventional computed tomography (CT), additionally focusing on correcting the partial volume effect - Forecasted needle trajectory in interventional CT images using estimates of model uncertainty- Explored use of adversarial models in generating contrast-enhanced CT images from non-contrast imagesPublications:- Pinnock… Show more Thesis: "Super-Resolution and Contrast Enhancement in Interventional Computed Tomography"- Performed super-resolution work to improve image quality in interventional computed tomography (CT), additionally focusing on correcting the partial volume effect - Forecasted needle trajectory in interventional CT images using estimates of model uncertainty- Explored use of adversarial models in generating contrast-enhanced CT images from non-contrast imagesPublications:- Pinnock MA et al. Multi-phase synthetic contrast enhancement in interventional computed tomography for guiding renal cryotherapy. IJCARS. 2023: 1-13- Pinnock MA et al. Combined 3D super-resolution, de-noising and partial volume correction for percutaneous ablation. Medical Imaging 2021: Image Processing. SPIE, 2021- Pinnock MA et al. End-to-end forecasting of needle trajectory in percutaneous ablation. Medical Image 2021: Image-Guided Procedures, Robot Interventions, and Modelling. SPIE, 2021 Show less

London

Dissertation: "CNN-based 3D Super-Resolution for Interventional CT"- Researched super-resolution in interventional computed tomography using convolutional neural networks- Tested effect of model complexity and use of spatial frequency-based regularisation on performance

London

Doctor Of Philosophy - Phd, Biomedical/Medical Engineering

Post-graduate teaching assistant: - Introductory Programming - Information Processing for Medical Imaging - Computational Modelling.

Master'S Degree, Medical Imaging

Modules taken: - Computational MRI - Research Software Engineering with Python - Information Processing for Medical Imaging.

Bachelor Of Medicine, Bachelor Of Surgery (Mbbs), Medicine

Bachelor Of Science (Bsc), Microbiology In Relation To Medicine