I obtained my BEng (Hons) in Mechanical Engineering from University of Dundee in 2019 and my MSc in Machine Learning and Deep Learning from University of Strathclyde in 2020. My MSc thesis titled ‘Deep learning methods for classification of low-resolution SAR-images’ focused on the use of GANs and CNNs on radar data.
I chose the CDT due to the collaborative and supportive research environment, as well as the strong links to industry through the National Innovation Centre for Data. I hope to learn from other members of the CDT outside of my research area to broaden my knowledge of work done in other domains. In my free time I enjoy going to the gym, listening to music and playing video-games.
Deep learning approach for comprehensive quantification of diabetic maculopathy from spectral-domain optical coherence tomography images
Diabetic eye disease accounts for over 28,000 people registered blind or sight-impaired in the UK, with 1280 new patients registered each year. The commonest cause of blindness in diabetics is macular oedema, which can be treated with laser, intravitreal injections of anti-VEGF drugs and steroids. Spectral-domain optical coherence tomography (SD-OCT) can quickly, painlessly and easily detect diabetic macular oedema (DMO), but current quantitative measures of its severity on SD-OCT are poor at predicting progression and response to treatment. Within my project I aim to develop a fully automated comprehensive tool to segment and quantify a range of different OCT biomarkers in diabetic macular oedema, to assess the performance of the tool against human grader classification and to assess the ability of the automated quantification to predict outcome after DMO treatment.
