  Acute aortic syndrome (AAS) is a group of life threatening conditions of the
aorta. We have developed an end-to-end automatic approach to detect AAS in
computed tomography (CT) images. Our approach consists of two steps. At first,
we extract N cross sections along the segmented aorta centerline for each CT
scan. These cross sections are stacked together to form a new volume which is
then classified using two different classifiers, a 3D convolutional neural
network (3D CNN) and a multiple instance learning (MIL). We trained, validated,
and compared two models on 2291 contrast CT volumes. We tested on a set aside
cohort of 230 normal and 50 positive CT volumes. Our models detected AAS with
an Area under Receiver Operating Characteristic curve (AUC) of 0.965 and 0.985
using 3DCNN and MIL, respectively.
