Engineering Commons^™

Systems And Methods For Scalable Retinal Screening Programs, Jeremy Richard Benson

Computer Science ETDs

This dissertation addresses gaps in artificial intelligence-based computer vision tasks in the medical image processing field. We demonstrate effective methods for standardizing and augmenting digital fundus photographs so that robust convolutional neural network-based systems can perform high-throughput disease classification and generalize to never-before-seen data from novel camera technologies, scaling with the changing hardware landscape, as well as keeping up with vast amount of incoming data from the ever-increasing population. We also tackle the problem of discovering relevant samples in an unlabeled cohort of image data, thus widening the bottleneck to all downstream supervised machine learning tasks.