论文标题
学习细粒性糖尿病性视网膜病等级的判别性表示
Learning Discriminative Representations for Fine-Grained Diabetic Retinopathy Grading
论文作者
论文摘要
糖尿病性视网膜病（DR）是失明的主要原因之一。但是，没有早期DR的特定症状会导致诊断延迟，从而导致患者的疾病进展。为了确定疾病的严重程度，眼科医生需要专注于眼底图像的歧视部分。近年来，深度学习在医学图像分析中取得了巨大成功。但是，大多数作品直接基于卷积神经网络（CNN）采用算法，该算法忽略了阶级之间的差异是微妙而逐渐的事实。因此，我们将DR的自动图像分级视为一项细粒度的分类任务，并构建双线性模型以识别病理学上的歧视区域。为了利用类之间的序数信息，我们使用序数回归方法获得软标签。此外，除了仅使用分类损失训练我们的网络外，我们还引入了度量损失，以学习更具歧视性的特征空间。实验结果表明，在两个公共IDRID和DEEPDR数据集上，提出的方法的出色性能。
Diabetic retinopathy (DR) is one of the leading causes of blindness. However, no specific symptoms of early DR lead to a delayed diagnosis, which results in disease progression in patients. To determine the disease severity levels, ophthalmologists need to focus on the discriminative parts of the fundus images. In recent years, deep learning has achieved great success in medical image analysis. However, most works directly employ algorithms based on convolutional neural networks (CNNs), which ignore the fact that the difference among classes is subtle and gradual. Hence, we consider automatic image grading of DR as a fine-grained classification task, and construct a bilinear model to identify the pathologically discriminative areas. In order to leverage the ordinal information among classes, we use an ordinal regression method to obtain the soft labels. In addition, other than only using a categorical loss to train our network, we also introduce the metric loss to learn a more discriminative feature space. Experimental results demonstrate the superior performance of the proposed method on two public IDRiD and DeepDR datasets.
