Multiscale High-Level Feature Fusion for Histopathological Image Classification

Computational and Mathematical Methods in Medicine
ZhiFei Lai, HuiFang Deng

Abstract

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.

References

Jul 25, 2015·IEEE Transactions on Medical Imaging·Jun XuAnant Madabhushi
Oct 30, 2015·IEEE Transactions on Medical Imaging·Tiep Huu VuU K Arvind Rao
Feb 11, 2016·IEEE Transactions on Medical Imaging·Korsuk SirinukunwattanaNasir M Rajpoot
May 24, 2016·Scientific Reports·Geert LitjensJeroen van der Laak

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Methods Mentioned

BETA
PCA
biopsy

Software Mentioned

Matlab
MatConvNet
MSAE
MPCA

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