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.