تقرير
Improving Lesion Segmentation for Diabetic Retinopathy using Adversarial Learning
| العنوان: | Improving Lesion Segmentation for Diabetic Retinopathy using Adversarial Learning |
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| المؤلفون: | Xiao, Qiqi, Zou, Jiaxu, Yang, Muqiao, Gaudio, Alex, Kitani, Kris, Smailagic, Asim, Costa, Pedro, Xu, Min |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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. Comment: Accepted to International Conference on Image Analysis and Recognition, ICIAR 2019. Published at https://doi.org/10.1007/978-3-030-27272-2_29 Code: https://github.com/zoujx96/DR-segmentation |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1007/978-3-030-27272-2_29 |
| URL الوصول: | http://arxiv.org/abs/2007.13854 |
| رقم الأكسشن: | edsarx.2007.13854 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1007/978-3-030-27272-2_29 |
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