Diabetic Retinopathy (DR) grading is challenging due to the presence of intra-class variations, small lesions and imbalanced data distributions. The key for solving fine-grained DR grading is to find more discriminative features corresponding to subtle visual differences, such as microaneurysms, hemorrhages and soft exudates. However, small lesions are quite difficult to identify using traditional convolutional neural networks (CNNs), and an imbalanced DR data distribution will cause the model to pay too much attention to DR grades with more samples, greatly affecting the final grading performance. In this article, we focus on developing an attention module to address these issues. Specifically, for imbalanced DR data distributions, we propose a novel Category Attention Block (CAB), which explores more discriminative region-wise features for each DR grade and treats each category equally. In order to capture more detailed small lesion information, we also propose the Global Attention Block (GAB), which can exploit detailed and class-agnostic global attention feature maps for fundus images. By aggregating the attention blocks with a backbone network, the CABNet is constructed for DR grading. The attention blocks can be applied to a wide range of backbone networks and trained efficiently in an end-to-end manner. Comprehensive experiments are conducted on three publicly available datasets, showing that CABNet produces significant performance improvements for existing state-of-the-art deep architectures with few additional parameters and achieves the state-of-the-art results for DR grading. Code and models will be available at https://github.com/he2016012996/CABnet .

中文翻译：

---

CABNet：糖尿病视网膜病变分级不平衡的类别注意块。

由于存在组内变异、小病变和不平衡的数据分布，糖尿病视网膜病变 (DR) 分级具有挑战性。解决细粒度DR分级的关键是找到与细微视觉差异相对应的更多判别特征，例如微动脉瘤、出血和软渗出物。然而，使用传统的卷积神经网络（CNN）很难识别小病灶，并且不平衡的 DR 数据分布会导致模型过多关注样本较多的 DR 分级，极大影响最终的分级性能。在本文中，我们重点开发一个注意力模块来解决这些问题。具体来说，对于不平衡的 DR 数据分布，我们提出了一种新颖的类别注意块（CAB），它为每个 DR 等级探索更具辨别力的区域特征，并平等地对待每个类别。为了捕获更详细的小病变信息，我们还提出了全局注意块（GAB），它可以利用眼底图像的详细且与类别无关的全局注意特征图。通过将注意力块与主干网络聚合，构建 CABNet 用于 DR 分级。注意力块可以应用于广泛的骨干网络，并以端到端的方式进行有效的训练。在三个公开可用的数据集上进行的综合实验表明，CABNet 只需很少的额外参数即可为现有最先进的深度架构带来显着的性能改进，并实现 DR 分级的最先进结果。代码和型号可在https://github.com/he2016012996/CABnet 。