论文标题
DRDR：使用Mask R-CNN和转移学习引起的渗出液和微动瘤的自动掩盖
DRDr: Automatic Masking of Exudates and Microaneurysms Caused By Diabetic Retinopathy Using Mask R-CNN and Transfer Learning
论文作者
论文摘要
本文解决了糖尿病患者眼中鉴定由糖尿病性视网膜病（DR）引起的两种主要类型病变类型的问题。我们利用卷积神经网络（CNN）和转移学习来定位和生成高质量的分割掩模，用于在患者的眼底图像中可以找到的每种病变实例。我们从e-ophtha ex和e-ophtha mA中创建了归一化的数据库，并调整了蒙版R-CNN来检测小病变。此外，我们采用数据增强和RESNET101的预先训练的权重来补偿我们的小数据集。我们的模型达到了0.45的有希望的测试图，完全表明它可以在检测和治疗臭名昭著的DR的过程中帮助临床医生和眼科医生。
This paper addresses the problem of identifying two main types of lesions - Exudates and Microaneurysms - caused by Diabetic Retinopathy (DR) in the eyes of diabetic patients. We make use of Convolutional Neural Networks (CNNs) and Transfer Learning to locate and generate high-quality segmentation mask for each instance of the lesion that can be found in the patients' fundus images. We create our normalized database out of e-ophtha EX and e-ophtha MA and tweak Mask R-CNN to detect small lesions. Moreover, we employ data augmentation and the pre-trained weights of ResNet101 to compensate for our small dataset. Our model achieves promising test mAP of 0.45, altogether showing that it can aid clinicians and ophthalmologist in the process of detecting and treating the infamous DR.
