  Optical Coherence Tomography (OCT) imaging plays an important role in
glaucoma diagnosis in clinical practice. Early detection and timely treatment
can prevent glaucoma patients from permanent vision loss. However, only a
dearth of automated methods has been developed based on OCT images for glaucoma
study. In this paper, we present a novel framework to effectively classify
glaucoma OCT images from normal ones. A semi-supervised learning strategy with
smoothness assumption is applied for surrogate assignment of missing function
regression labels. Besides, the proposed multi-task learning network is capable
of exploring the structure and function relationship from the OCT image and
visual field measurement simultaneously, which contributes to classification
performance boosting. Essentially, we are the first to unify the structure
analysis and function regression for glaucoma screening. It is also worth
noting that we build the largest glaucoma OCT image dataset involving 4877
volumes to develop and evaluate the proposed method. Extensive experiments
demonstrate that our framework outperforms the baseline methods and two
glaucoma experts by a large margin, achieving 93.2%, 93.2% and 97.8% on
accuracy, F1 score and AUC, respectively.
