Biomedical Engineering Group, University of Valladolid, Valladolid, 47011, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain.
Biomedical Engineering Group, University of Valladolid, Valladolid, 47011, Spain.
Comput Methods Programs Biomed. 2024 Jun;249:108160. doi: 10.1016/j.cmpb.2024.108160. Epub 2024 Apr 3.
Early detection and grading of Diabetic Retinopathy (DR) is essential to determine an adequate treatment and prevent severe vision loss. However, the manual analysis of fundus images is time consuming and DR screening programs are challenged by the availability of human graders. Current automatic approaches for DR grading attempt the joint detection of all signs at the same time. However, the classification can be optimized if red lesions and bright lesions are independently processed since the task gets divided and simplified. Furthermore, clinicians would greatly benefit from explainable artificial intelligence (XAI) to support the automatic model predictions, especially when the type of lesion is specified. As a novelty, we propose an end-to-end deep learning framework for automatic DR grading (5 severity degrees) based on separating the attention of the dark structures from the bright structures of the retina. As the main contribution, this approach allowed us to generate independent interpretable attention maps for red lesions, such as microaneurysms and hemorrhages, and bright lesions, such as hard exudates, while using image-level labels only.
Our approach is based on a novel attention mechanism which focuses separately on the dark and the bright structures of the retina by performing a previous image decomposition. This mechanism can be seen as a XAI approach which generates independent attention maps for red lesions and bright lesions. The framework includes an image quality assessment stage and deep learning-related techniques, such as data augmentation, transfer learning and fine-tuning. We used the architecture Xception as a feature extractor and the focal loss function to deal with data imbalance.
The Kaggle DR detection dataset was used for method development and validation. The proposed approach achieved 83.7 % accuracy and a Quadratic Weighted Kappa of 0.78 to classify DR among 5 severity degrees, which outperforms several state-of-the-art approaches. Nevertheless, the main result of this work is the generated attention maps, which reveal the pathological regions on the image distinguishing the red lesions and the bright lesions. These maps provide explainability to the model predictions.
Our results suggest that our framework is effective to automatically grade DR. The separate attention approach has proven useful for optimizing the classification. On top of that, the obtained attention maps facilitate visual interpretation for clinicians. Therefore, the proposed method could be a diagnostic aid for the early detection and grading of DR.
早期发现和分级糖尿病视网膜病变（DR）对于确定适当的治疗方法和防止严重视力丧失至关重要。然而，眼底图像的手动分析非常耗时，DR 筛查计划也受到人类分级员可用性的限制。目前用于 DR 分级的自动方法尝试同时联合检测所有迹象。然而，如果独立处理红色病变和明亮病变，则可以优化分类，因为任务会被划分和简化。此外，临床医生将极大地受益于可解释的人工智能（XAI）来支持自动模型预测，特别是当指定病变类型时。作为新颖性，我们提出了一种基于分离视网膜的暗结构和亮结构注意力的端到端深度学习框架，用于自动 DR 分级（5 个严重程度）。作为主要贡献，这种方法允许我们仅使用图像级标签为红色病变（如微动脉瘤和出血）和明亮病变（如硬性渗出物）生成独立的可解释注意力图。
我们的方法基于一种新颖的注意力机制，该机制通过执行图像分解来分别关注视网膜的暗结构和亮结构。这种机制可以看作是一种 XAI 方法，它为红色病变和明亮病变生成独立的注意力图。该框架包括图像质量评估阶段和深度学习相关技术，如数据增强、迁移学习和微调。我们使用 Xception 架构作为特征提取器，并使用焦点损失函数来处理数据不平衡。
我们使用 Kaggle DR 检测数据集进行方法开发和验证。所提出的方法在分类 5 个严重程度的 DR 时达到了 83.7%的准确率和 0.78 的二次加权 Kappa，优于几种最先进的方法。然而，这项工作的主要结果是生成的注意力图，它可以区分图像上的病理区域，区分红色病变和明亮病变。这些地图为模型预测提供了可解释性。
我们的结果表明，我们的框架可以有效地自动分级 DR。单独的注意力方法已被证明有助于优化分类。除此之外，获得的注意力图便于临床医生进行视觉解释。因此，所提出的方法可以成为早期发现和分级 DR 的诊断辅助工具。
