Classification of lung adenocarcinoma transcriptome subtypes from pathological images using deep convolutional networks

Purpose Convolutional neural networks have become rapidly popular for image recognition and image analysis because of its powerful potential. In this paper, we developed a method for classifying subtypes of lung adenocarcinoma from pathological images using neural network whose that can evaluate phe...

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Bibliographic Details
Published in:	International journal for computer assisted radiology and surgery Vol. 13; no. 12; pp. 1905 - 1913
Main Authors:	Antonio, Victor Andrew A., Ono, Naoaki, Saito, Akira, Sato, Tetsuo, Altaf-Ul-Amin, Md, Kanaya, Shigehiko
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 01.12.2018 Springer Nature B.V
Subjects:	Adenocarcinoma of Lung - classification Adenocarcinoma of Lung - diagnosis Adenocarcinoma of Lung - genetics Artificial neural networks Biopsy Classification Computer Imaging Computer Science Entropy (Information theory) Feature extraction Feature recognition Health Informatics Humans Image analysis Image classification Image Processing, Computer-Assisted - methods Imaging Medicine Medicine & Public Health Neural networks Neural Networks (Computer) Object recognition Original Original Article Pattern recognition Pattern Recognition and Graphics Radiology Surgery Transcriptome Tumors Vision Deep learning Autoencoder Independent subspace analysis Lung cancer Computer-aided diagnosis
ISSN:	1861-6410, 1861-6429, 1861-6429
Online Access:	Get full text
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