Automated segmentation of geographic atrophy using deep convolutional neural networks

2018 ◽  
Author(s):  
Zhihong Hu ◽  
Ziyuan Wang ◽  
SriniVas Sadda
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Geographic Atrophy ◽  
Automated Segmentation ◽  
Deep Convolutional Neural Networks

scholarly journals Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Khaled Bousabarah ◽  
Maximilian Ruge ◽  
Julia-Sarita Brand ◽  
Mauritius Hoevels ◽  
Daniel Rueß ◽  
...  
Keyword(s):  
Neural Networks ◽  
Brain Metastases ◽  
Convolutional Neural Networks ◽  
Clinical Data ◽  
Automated Segmentation ◽  
Deep Convolutional Neural Networks

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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