scholarly journals Improving Classification of Breast Cancer by Utilizing the Image Pyramids of Whole-Slide Imaging and Multi-scale Convolutional Neural Networks

2019 ◽  
Author(s):  
Li Tong ◽  
Ying Sha ◽  
May D Wang
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Whole Slide Imaging ◽  
Multi Scale ◽  
Image Pyramids

scholarly journals Multi-scale Convolutional Neural Networks for Classification of Digital Mammograms with Breast Calcifications

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Chatsuda Songsaeng ◽  
Piyanoot Woodtichartpreecha ◽  
Sitthichok Chaichulee
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Multi Scale

scholarly journals Classification of breast cancer histology images using Convolutional Neural Networks

PLoS ONE ◽  
2017 ◽  
Vol 12 (6) ◽  
pp. e0177544 ◽  
Author(s):  
Teresa Araújo ◽  
Guilherme Aresta ◽  
Eduardo Castro ◽  
José Rouco ◽  
Paulo Aguiar ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Convolutional Neural Networks

scholarly journals Classification of Breast Cancer Based on Histology Images Using Convolutional Neural Networks

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 24680-24693 ◽  
Author(s):  
Dalal Bardou ◽  
Kun Zhang ◽  
Sayed Mohammad Ahmad
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Convolutional Neural Networks

EMS-Net: Ensemble of Multiscale Convolutional Neural Networks for Classification of Breast Cancer Histology Images

2019 ◽  
Vol 366 ◽  
pp. 46-53 ◽  
Author(s):  
Zhanbo Yang ◽  
Lingyan Ran ◽  
Shizhou Zhang ◽  
Yong Xia ◽  
Yanning Zhang
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
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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