33 datascEYEnce! Computer Vision News The trajectories have an agnostic number of points and therefore it is difficult to find an average value for representation. So, what is the final outcome? Robbie and his collaborators were surprised at how well contrastive learning can model human concepts. With the help of their deep learning pipeline, they were able to isolate patterns of disease progression that are suspected by clinicians to be related to AMD. In other words, the clinicians were able to relate the automatically generated clusters to known and additionally yet unknown temporal biomarkers. As a last question, I asked Robbie about advice for prospective PhD students. For everyone who also wants to pursue research in deep learning for ophthalmology, Robbie emphasized research in selfsupervised learning applied to retinal images. It is an exciting field, where you can try new big ideas - another very good example is RetFound which was recently published by researchers from UCL/Moorfields. In general, there is a high demand for understanding eye-related diseases and a lot of problems that remain to be solved!! More about AI for Ophthalmology Do you enjoy this November issue of Computer Vision News? We are glad that you do! We have one more important community message to tell you. It’s an advert for something free ☺ Just go to page 64, and you’ll know. Keep in touch!
RkJQdWJsaXNoZXIy NTc3NzU=