论文标题
在糖尿病性视网膜病变中的病变定位
Towards the Localisation of Lesions in Diabetic Retinopathy
论文作者
论文摘要
近期卷积神经网络（CNN）已成功地用于对糖尿病性视网膜病（DR）眼底图像进行分类。但是，CNN的更深层表示可能以空间分辨率为代价捕获更高级别的语义。为了制作可用于眼科医生的预测，我们使用一种在深度学习模型的倒数第二层上称为梯度加权类激活映射（Grad-CAM）的注意力后技术，以在DR Feldus图像上产生粗糙的本地化图。这是为了帮助确定图像中的歧视区域，因此为眼科医生提供了诊断并有可能通过早期诊断挽救生命的证据。具体而言，这项研究使用了四个最先进的深度学习模型的预训练权重来生成和比较底眼图像的本地化图。使用的模型包括VGG16，RESNET50，InceptionV3和InceptionResnetV2。我们发现，InceptionV3的测试分类精度为96.07％，与其他模型相比，测试分类精度达到96.07％，并更快地定位病变。
Convolutional Neural Networks (CNNs) have successfully been used to classify diabetic retinopathy (DR) fundus images in recent times. However, deeper representations in CNNs may capture higher-level semantics at the expense of spatial resolution. To make predictions usable for ophthalmologists, we use a post-attention technique called Gradient-weighted Class Activation Mapping (Grad-CAM) on the penultimate layer of deep learning models to produce coarse localisation maps on DR fundus images. This is to help identify discriminative regions in the images, consequently providing evidence for ophthalmologists to make a diagnosis and potentially save lives by early diagnosis. Specifically, this study uses pre-trained weights from four state-of-the-art deep learning models to produce and compare localisation maps of DR fundus images. The models used include VGG16, ResNet50, InceptionV3, and InceptionResNetV2. We find that InceptionV3 achieves the best performance with a test classification accuracy of 96.07%, and localise lesions better and faster than the other models.
