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}.

翻译：青光眼是全球不可逆失明的首要原因。准确检测青光眼及其进展预测面临的主要瓶颈在于，采用最先进的3D视网膜成像技术——光学相干断层扫描（OCT）获得的标记患者数据有限。为解决数据稀缺问题，本文提出两种方案。首先，我们开发了一种名为伪监督器的新型泛化增强半监督学习（SSL）模型，以最优方式利用未标记数据。与现有最先进模型相比，所提出的伪监督器通过优化利用未标记样本预测伪标签的策略，提升了经验泛化能力。我们的伪监督器模型在青光眼检测和进展预测两项临床任务上进行了评估。进展预测任务分别在单模态和多模态下进行测试。伪监督器模型展现出优于最先进SSL对比模型的性能。此外，该模型在公开的LAG眼底数据集上也取得了最佳结果。其次，我们引入了哈佛青光眼检测与进展（Harvard-GDP）数据集，这是一个包含1000例患者OCT成像数据及其青光眼检测与进展标签的多模态多任务数据集。这是目前最大的包含3D OCT成像数据的青光眼检测数据集，也是首个公开可用的青光眼进展预测数据集。我们提供了详细的性别和种族分析数据，可供感兴趣的研究者用于公平性学习研究。此外，我们采用多种最先进的监督学习CNN和Transformer深度学习模型对所发布数据集进行了基准测试。数据集和代码已通过网址 \url{https://ophai.hms.harvard.edu/datasets/harvard-gdp1000} 公开提供。