An Approach to Semantic Segmentation of Retinal Images Using Deep Neural Networks for Mapping Laser Exposure Zones for the Treatment of Diabetic Macular Edema

2021 ◽  
pp. 106-116
Author(s):  
Nataly Yu. Ilyasova ◽  
Rustam A. Paringer ◽  
Alexander S. Shirokanev ◽  
Nikita S. Demin
Keyword(s):  
Neural Networks ◽  
Macular Edema ◽  
Diabetic Macular Edema ◽  
Deep Neural Networks ◽  
Semantic Segmentation ◽  
Retinal Images ◽  
Laser Exposure

scholarly journals Diabetic Macular Edema Grading Based on Deep Neural Networks

2016 ◽  
Author(s):  
Baidaa Al-Bander ◽  
Waleed Al-Nuaimy ◽  
Majid A. Al-Taee ◽  
Bryan M. Williams ◽  
Yalin Zheng
Keyword(s):  
Neural Networks ◽  
Macular Edema ◽  
Diabetic Macular Edema ◽  
Deep Neural Networks

scholarly journals Semantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery

2021 ◽  
Author(s):  
Lucas Prado Osco ◽  
Keiller Nogueira ◽  
Ana Paula Marques Ramos ◽  
Mayara Maezano Faita Pinheiro ◽  
Danielle Elis Garcia Furuya ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Semantic Segmentation ◽  
Citrus Orchard

Semantic Segmentation of Mammograms Using Pre-Trained Deep Neural Networks

2021 ◽  
Author(s):  
Rodrigo Leite Prates ◽  
Wilfrido Gomez-Flores ◽  
Wagner Pereira
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Semantic Segmentation

Automated segmentation of macular edema in OCT using deep neural networks

2019 ◽  
Vol 55 ◽  
pp. 216-227 ◽  
Author(s):  
Junjie Hu ◽  
Yuanyuan Chen ◽  
Zhang Yi
Keyword(s):  
Neural Networks ◽  
Macular Edema ◽  
Deep Neural Networks ◽  
Automated Segmentation

scholarly journals SEMI-SUPERVISED SEMANTIC SEGMENTATION NETWORK VIA LEARNING CONSISTENCY FOR REMOTE SENSING LAND-COVER CLASSIFICATION

2020 ◽  
Vol V-2-2020 ◽  
pp. 609-615
Author(s):  
B. Zhang ◽  
Y. Zhang ◽  
Y. Li ◽  
Y. Wan ◽  
F. Wen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Land Cover ◽  
Deep Neural Networks ◽  
Semantic Segmentation ◽  
Land Cover Classification ◽  
Unlabeled Data ◽  
Weighted Sum ◽  
Affine Transform

Abstract. Current popular deep neural networks for semantic segmentation are almost supervised and highly rely on a large amount of labeled data. However, obtaining a large amount of pixel-level labeled data is time-consuming and laborious. In remote sensing area, this problem is more urgent. To alleviate this problem, we propose a novel semantic segmentation neural network (S4Net) based on semi-supervised learning by using unlabeled data. Our model can learn from unlabeled data by consistency regularization, which enforces the consistency of output under different random transforms and perturbations, such as random affine transform. Thus, the network is trained by the weighted sum of a supervised loss from labeled data and a consistency regularization loss from unlabeled data. The experiments we conducted on DeepGlobe land cover classification challenge dataset verified that our network can make use of unlabeled data to obtain precise results of semantic segmentation and achieve competitive performance when compared to other methods.

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