Inferring Disease Status by Non-parametric Probabilistic Embedding

Abstract

Computing similarity between all pairs of patients in a dataset enables us to group the subjects into disease subtypes and infer their disease status. However, robust and efficient computation of pairwise similarity is a challenging task for large-scale medical image datasets. We specifically target diseases where multiple subtypes of pathology present simultaneously, rendering the definition of the similarity a difficult task. To define pairwise patient similarity, we characterize each subject by a probability distribution that generates its local image descriptors. We adopt a notion of affinity between probability distributions which lends itself to similarity between subjects. Instead of approximating the distributions by a parametric family, we propose to compute the affinity measure indirectly using an approximate nearest neighbor estimator. Computing pairwise similarities enables us to embed the entire patient population into a lower dimensional manifold, mapping each subject from high-dimensional image space to an informative low-dimensional representation. We validate our method on a large-scale lung CT scan study and demonstrate the state-of-the-art prediction on an important physiologic measure of airflow (the forced expiratory volume in one second, FEV1) in addition to a 5-category clinical rating (so-called GOLD score).