Nuclear cataract (NC) is a leading eye disease for blindness and vision impairment globally. Accurate and objective NC grading/classification is essential for clinically early intervention and cataract surgery planning. Anterior segment optical coherence tomography (AS-OCT) images are capable of capturing the nucleus region clearly and measuring the opacity of NC quantitatively. Recently, clinical research has suggested that the opacity correlation and repeatability between NC severity levels and the average nucleus density on AS-OCT images is high with the interclass and intraclass analysis. Moreover, clinical research has suggested that opacity distribution is uneven on the nucleus region, indicating that the opacities from different nucleus regions may play different roles in NC diagnosis. Motivated by the clinical priors, this paper proposes a simple yet effective region-based integration-and-recalibration attention (RIR), which integrates multiple feature map region representations and recalibrates the weights of each region via softmax attention adaptively. This region recalibration strategy enables the network to focus on high contribution region representations and suppress less useful ones. We combine the RIR block with the residual block to form a Residual-RIR module, and then a sequence of Residual-RIR modules are stacked to a deep network named region-based integration-and-recalibration network (RIR-Net), to predict NC severity levels automatically. The experiments on a clinical AS-OCT image dataset and two OCT datasets demonstrate that our method outperforms strong baselines and previous state-of-the-art methods. Furthermore, attention weight visualization analysis and ablation studies verify the capability of our RIR-Net for adjusting the relative importance of different regions in feature maps dynamically, agreeing with the clinical research.

核性白内障 (NC) 是全球导致失明和视力障碍的主要眼病。准确和客观的 NC 分级/分类对于临床早期干预和白内障手术计划至关重要。眼前节光学相干断层扫描 (AS-OCT) 图像能够清晰地捕捉核区域并定量测量 NC 的不透明度。最近，临床研究表明，通过类间和类内分析，NC 严重程度与 AS-OCT 图像上的平均核密度之间的不透明度相关性和可重复性很高。此外，临床研究表明，核区混浊分布不均匀，表明不同核区的混浊在 NC 诊断中可能发挥不同的作用。受临床先验的启发，本文提出了一种简单而有效的基于区域的集成和重新校准注意力（RIR），它集成了多个特征图区域表示，并通过softmax注意力自适应地重新校准每个区域的权重。这种区域重新校准策略使网络能够专注于高贡献区域表示并抑制不太有用的区域表示。我们将 RIR 块与残差块组合形成一个 Residual-RIR 模块，然后将一系列 Residual-RIR 模块堆叠到一个名为基于区域的集成和重新校准网络 (RIR-Net) 的深度网络中，以预测NC 严重性级别自动。对临床 AS-OCT 图像数据集和两个 OCT 数据集的实验表明，我们的方法优于强大的基线和以前的最先进方法。此外，