  Glaucoma is the number one cause of irreversible blindness globally. A major
challenge for accurate glaucoma detection and progression forecasting is the
bottleneck of limited labeled patients with the state-of-the-art (SOTA) 3D
retinal imaging data of optical coherence tomography (OCT). To address the data
scarcity issue, this paper proposes two solutions. First, we develop a novel
generalization-reinforced semi-supervised learning (SSL) model called pseudo
supervisor to optimally utilize unlabeled data. Compared with SOTA models, the
proposed pseudo supervisor optimizes the policy of predicting pseudo labels
with unlabeled samples to improve empirical generalization. Our pseudo
supervisor model is evaluated with two clinical tasks consisting of glaucoma
detection and progression forecasting. The progression forecasting task is
evaluated both unimodally and multimodally. Our pseudo supervisor model
demonstrates superior performance than SOTA SSL comparison models. Moreover,
our model also achieves the best results on the publicly available LAG fundus
dataset. Second, we introduce the Harvard Glaucoma Detection and Progression
(Harvard-GDP) Dataset, a multimodal multitask dataset that includes data from
1,000 patients with OCT imaging data, as well as labels for glaucoma detection
and progression. This is the largest glaucoma detection dataset with 3D OCT
imaging data and the first glaucoma progression forecasting dataset that is
publicly available. Detailed sex and racial analysis are provided, which can be
used by interested researchers for fairness learning studies. Our released
dataset is benchmarked with several SOTA supervised CNN and transformer deep
learning models. The dataset and code are made publicly available via
\url{https://ophai.hms.harvard.edu/datasets/harvard-gdp1000}.
