Glaucoma is one of the leading causes of blindness worldwide and Optical Coherence Tomography (OCT) is the quintessential imaging technique for its detection. Unlike most of the state-of-the-art studies focused on glaucoma detection, in this paper, we propose, for the first time, a novel framework for glaucoma grading using raw circumpapillary B-scans. In particular, we set out a new OCT-based hybrid network which combines hand-driven and deep learning algorithms. An OCT-specific descriptor is proposed to extract hand-crafted features related to the retinal nerve fibre layer (RNFL). In parallel, an innovative CNN is developed using skip-connections to include tailored residual and attention modules to refine the automatic features of the latent space. The proposed architecture is used as a backbone to conduct a novel few-shot learning based on static and dynamic prototypical networks. The k-shot paradigm is redefined giving rise to a supervised end-to-end system which provides substantial improvements discriminating between healthy, early and advanced glaucoma samples. The training and evaluation processes of the dynamic prototypical network are addressed from two fused databases acquired via Heidelberg Spectralis system. Validation and testing results reach a categorical accuracy of 0.9459 and 0.8788 for glaucoma grading, respectively. Besides, the high performance reported by the proposed model for glaucoma detection deserves a special mention. The findings from the class activation maps are directly in line with the clinicians' opinion since the heatmaps pointed out the RNFL as the most relevant structure for glaucoma diagnosis.

青光眼是全球失明的主要原因之一，光学相干断层扫描 (OCT) 是其检测的典型成像技术。与大多数专注于青光眼检测的最新研究不同，在本文中，我们首次提出了一种使用原始环乳头 B 扫描进行青光眼分级的新框架。特别是，我们提出了一个新的基于 OCT 的混合网络，它结合了手动和深度学习算法。提出了一种特定于 OCT 的描述符来提取与视网膜神经纤维层 (RNFL) 相关的手工特征。同时，使用跳跃连​​接开发了一种创新的 CNN，包括定制的残差和注意力模块，以改进潜在空间的自动特征。所提出的架构用作主干，以进行基于静态和动态原型网络的新型小样本学习。这k- shot 范式被重新定义，从而产生了受监督的端到端系统，该系统提供了区分健康、早期和晚期青光眼样本的实质性改进。动态原型网络的训练和评估过程由通过 Heidelberg Spectralis 系统获取的两个融合数据库处理。验证和测试结果对青光眼分级的分类准确度分别达到 0.9459 和 0.8788。此外，所提出的青光眼检测模型报告的高性能值得特别提及。类激活图的发现与临床医生的意见直接一致，因为热图指出 RNFL 是青光眼诊断最相关的结构。