scholarly journals Residual Convolutional Neural Networks for Breast Density Classification

2019 ◽  
Author(s):  
Francesca Lizzi ◽  
Stefano Atzori ◽  
Giacomo Aringhieri ◽  
Paolo Bosco ◽  
Carolina Marini ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Breast Density

Abstract 4235: Application of convolutional neural networks to breast biopsies to uncover tissue correlates of mammographic breast density

2017 ◽  
Author(s):  
Maeve Mullooly ◽  
Babak Ehteshami Bejnordi ◽  
Maya Palakal ◽  
Pamela M. Vacek ◽  
Donald L. Weaver ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Breast Density ◽  
Mammographic Breast Density

Residual Convolutional Neural Networks to Automatically Extract Significant Breast Density Features

2019 ◽  
pp. 28-35
Author(s):  
Francesca Lizzi ◽  
Francesco Laruina ◽  
Piernicola Oliva ◽  
Alessandra Retico ◽  
Maria Evelina Fantacci
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Breast Density

scholarly journals Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks

2018 ◽  
Vol 156 ◽  
pp. 191-207 ◽  
Author(s):  
João Otávio Bandeira Diniz ◽  
Pedro Henrique Bandeira Diniz ◽  
Thales Levi Azevedo Valente ◽  
Aristófanes Corrêa Silva ◽  
Anselmo Cardoso de Paiva ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Breast Density

Traffic Lights Detection in adverse conditions using Convolutional Neural Networks

2018 ◽  
Author(s):  
George Symeonidis ◽  
Peter P. Groumpos ◽  
Evangelos Dermatas
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Lights ◽  
Adverse Conditions

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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