scholarly journals Evaluation of convolutional neural networks for search in 1/f2.8 filtered noise and digital breast tomosynthesis phantoms

2020 ◽  
Author(s):  
Aditya Jonnalagadda ◽  
Miguel A. Lago ◽  
Bruno Barufaldi ◽  
Predrag R. Bakic ◽  
Craig K. Abbey ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis ◽  
Filtered Noise

Application of neural networks to model the signal-dependent noise of a digital breast tomosynthesis unit

2018 ◽  
Author(s):  
Fabrício A. Brito ◽  
Lucas R. Borges ◽  
Marcelo A. C. Vieira ◽  
Igor Guerrero ◽  
Andrew D. A. Maidment ◽  
...  
Keyword(s):  
Neural Networks ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis

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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