Inferring Disease Status by non-Parametric Probabilistic Embedding

N. Batmanghelich, A. Saeedi, R. J. Estepar, M. Cho, S. Wells, Inferring Disease Status by non-Parametric Probabilistic Embedding. Workshop on Medical Computer Vision: Algorithms for Big Data (MCV), Held in Conjunction with International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), to be appear in LNCS, 2016.

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 challenging task for large-scale medical image datasets. We specifically target diseases where multiple subtypes of pathology present simultaneously, rendering the denition 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).