Multiscale High-Level Feature Fusion for Histopathological Image Classification

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 histopatholo...

Full description

Saved in:
Bibliographic Details
Published in:Computational and mathematical methods in medicine 2017-01, Vol.2017 (2017), p.1-6
Main Authors: Lai, ZhiFei, Deng, HuiFang
Format: Article
Language:English
Subjects:
Algorithms
Area Under Curve
Diagnosis, Computer-Assisted
Diagnostic Imaging
False Positive Reactions
Image Processing, Computer-Assisted - methods
Machine Learning
Models, Statistical
Neural Networks, Computer
Principal Component Analysis
Reproducibility of Results
ROC Curve
Software
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1748-670X
1748-6718
DOI:10.1155/2017/7521846