Computer Vision News 30 Deep Learning for the Eyes Leaving behind a world of completing Jira tickets as a software engineer led him to work on finding disease trajectories for AMD, which he was able to present at last month’s MICCAI in Vancouver! In case you missed out on it, I am covering the key points of “Clustering Disease Trajectories in Contrastive Feature Space for Biomarker Proposal in Age-Related Macular Degeneration” here! Let’s start with the application. Robbie explained the limitations of common grading systems for AMD to me - they lack the ability of prognostics. Simply said, it is unclear how long it will take until a patient transitions from early-stage to a late stage of AMD. Some patients progress quicker than others. by Christina Bornberg @datascEYEnce It is time for another deep learning in ophthalmology interview as part of the datascEYEnce column here in the Computer Vision News magazine! I am Christina and through my work with retinal images, I come across a lot of amazing research that I want to share with you! This time, I interviewed Robbie Holland from Imperial College London on his work on age-related macular degeneration (AMD)! featuring Robbie Holland Robbie's decision to get involved with deep learning for ophthalmology was influenced by both, his interest in modelling complex systems as well as reading a publication by DeepMind in 2018: “Clinically applicable deep learning for diagnosis and referral in retinal disease“. Following his undergrad in Mathematics and Computer Science as well as a project on abdominal MRI analysis, he started his PhD with a focus on the early detection of AMD under the supervision of Daniel Rueckert and Martin Menten in the BioMedIA lab, Imperial College London. Automated AMD biomarker discovery
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