Breast Cancer Classification From Histopathological Images Using Resolution Adaptive Network
Author:
Affiliation:
1. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
Funder
National Natural Science Foundation of China
Award Foundation of Chinese Academy of Sciences
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science
Link
http://xplorestaging.ieee.org/ielx7/6287639/9668973/09745527.pdf?arnumber=9745527
Reference46 articles.
1. Rectified linear units improve restricted Boltzmann machines Vinod Nair;nair;Proc 27th Int Conf Mach Learn (ICML),2010
2. Batch normalization: Accelerating deep network training by reducing internal covariate shift;ioffe;Proc Int Conf Mach Learn,2015
3. Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network
4. A tree-based multiclassification of breast tumor histopathology images through deep learning
5. Breast cancer multi-classification from histopathological images with structured deep learning model;han;Sci Rep,2017
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