Application of network techniques to image analysis and outlier detection

Recent advances in computer science and machine learning (ML) have generated highly efficient and unsupervised algorithms for the analysis of biomedical images, enabling cost-effective remote early detection of diseases. In this talk I will present various methods for ophthalmic image analysis, which use ML and network analysis. First, I will present a ML algorithm for the analysis of optical coherence tomography (OCT) images, which extracts features that discriminate between healthy and unhealthy subjects. Then, I will show that network analysis applied to the tree-like structure of the network of vessels in the retina returns features that discriminate between healthy subjects and those with glaucoma or diabetic retinopathy. Finally, I will discuss how the network percolation transition can be used for mining outliers in wide range of high-dimensional data sets, including ophthalmic images.