Improving Lesion Segmentation for Diabetic Retinopathy using Adversarial Learning

• URL: http://arxiv.org/abs/2007.13854v1
• Date: Mon, 27 Jul 2020 20:43:36 GMT
• Title: Improving Lesion Segmentation for Diabetic Retinopathy using Adversarial Learning
• Authors: Qiqi Xiao and Jiaxu Zou and Muqiao Yang and Alex Gaudio and Kris Kitani and Asim Smailagic and Pedro Costa and Min Xu
• Abstract summary: We propose an end-to-end system for pixel-level segmentation of Diabetic Retinopathy lesions using HEDNet and Conditional Generative Adversarial Network (cGAN) Our experiments show that the addition of the adversarial loss improves the lesion segmentation performance over the baseline.
• Score: 21.69817451167427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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