论文标题
使用对抗性学习改善糖尿病性视网膜病变的病变细分
Improving Lesion Segmentation for Diabetic Retinopathy using Adversarial Learning
论文作者
论文摘要
糖尿病性视网膜病（DR）是成年成年人失明的主要原因。病变博士在底面图像中可能具有挑战性，自动DR检测系统可以提供强大的临床价值。在DR的公开标记数据集中，印度糖尿病性视网膜病变图（IDRID）呈现了带有四个不同病变的像素级注释的视网膜底眼图像：微型神经疗法，出血，软渗出液和硬渗出物。我们利用Hednet边缘检测器在此数据集上求解语义分割任务，然后通过将Hednet纳入有条件的生成对抗网络（CGAN），为Pixel级分割而提出了一个用于像素级分割的端到端系统。我们设计了一个损失功能，为分割损失增加对抗性损失。我们的实验表明，对抗性损失的增加可改善基线的病变分割性能。
Diabetic Retinopathy (DR) is a leading cause of blindness in working age adults. DR lesions can be challenging to identify in fundus images, and automatic DR detection systems can offer strong clinical value. Of the publicly available labeled datasets for DR, the Indian Diabetic Retinopathy Image Dataset (IDRiD) presents retinal fundus images with pixel-level annotations of four distinct lesions: microaneurysms, hemorrhages, soft exudates and hard exudates. We utilize the HEDNet edge detector to solve a semantic segmentation task on this dataset, and then propose an end-to-end system for pixel-level segmentation of DR lesions by incorporating HEDNet into a Conditional Generative Adversarial Network (cGAN). We design a loss function that adds adversarial loss to segmentation loss. Our experiments show that the addition of the adversarial loss improves the lesion segmentation performance over the baseline.
