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Multiscale High-Level Feature Fusion for Histopathological Image Classification
Joint Authors
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-12-31
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
Histopathological image classification is one of the most important steps for disease diagnosis.
We proposed a method for multiclass histopathological image classification based on deep convolutional neural network referred to as coding network.
It can gain better representation for the histopathological image than only using coding network.
The main process is that training a deep convolutional neural network is to extract high-level feature and fuse two convolutional layers’ high-level feature as multiscale high-level feature.
In order to gain better performance and high efficiency, we would employ sparse autoencoder (SAE) and principal components analysis (PCA) to reduce the dimensionality of multiscale high-level feature.
We evaluate the proposed method on a real histopathological image dataset.
Our results suggest that the proposed method is effective and outperforms the coding network.
American Psychological Association (APA)
Lai, ZhiFei& Deng, HuiFang. 2017. Multiscale High-Level Feature Fusion for Histopathological Image Classification. Computational and Mathematical Methods in Medicine،Vol. 2017, no. 2017, pp.1-6.
https://search.emarefa.net/detail/BIM-1142303
Modern Language Association (MLA)
Lai, ZhiFei& Deng, HuiFang. Multiscale High-Level Feature Fusion for Histopathological Image Classification. Computational and Mathematical Methods in Medicine No. 2017 (2017), pp.1-6.
https://search.emarefa.net/detail/BIM-1142303
American Medical Association (AMA)
Lai, ZhiFei& Deng, HuiFang. Multiscale High-Level Feature Fusion for Histopathological Image Classification. Computational and Mathematical Methods in Medicine. 2017. Vol. 2017, no. 2017, pp.1-6.
https://search.emarefa.net/detail/BIM-1142303
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142303