  A major cause of irreversible visual impairment is angle-closure glaucoma,
which can be screened through imagery from Anterior Segment Optical Coherence
Tomography (AS-OCT). Previous computational diagnostic techniques address this
screening problem by extracting specific clinical measurements or handcrafted
visual features from the images for classification. In this paper, we instead
propose to learn from training data a discriminative representation that may
capture subtle visual cues not modeled by predefined features. Based on
clinical priors, we formulate this learning with a presented Multi-Context Deep
Network (MCDN) architecture, in which parallel Convolutional Neural Networks
are applied to particular image regions and at corresponding scales known to be
informative for clinically diagnosing angle-closure glaucoma. The output
feature maps of the parallel streams are merged into a classification layer to
produce the deep screening result. Moreover, we incorporate estimated clinical
parameters to further enhance performance. On a clinical AS-OCT dataset, our
system is validated through comparisons to previous screening methods.
