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Classify epithelium‐stroma in histopathological images based on deep transferable network
Author(s) -
YU X.,
ZHENG H.,
LIU C.,
HUANG Y.,
DING X.
Publication year - 2018
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/jmi.12705
Subject(s) - computer science , deep learning , artificial intelligence , convolutional neural network , pattern recognition (psychology) , stroma , domain adaptation , artificial neural network , deep neural networks , computer vision , pathology , classifier (uml) , medicine , immunohistochemistry
Summary Recently, the deep learning methods have received more attention in histopathological image analysis. However, the traditional deep learning methods assume that training data and test data have the same distributions, which causes certain limitations in real‐world histopathological applications. However, it is costly to recollect a large amount of labeled histology data to train a new neural network for each specified image acquisition procedure even for similar tasks. In this paper, an unsupervised domain adaptation is introduced into a typical deep convolutional neural network (CNN) model to mitigate the repeating of the labels. The unsupervised domain adaptation is implemented by adding two regularisation terms, namely the feature‐based adaptation and entropy minimisation, to the object function of a widely used CNN model called the AlexNet. Three independent public epithelium‐stroma datasets were used to verify the proposed method. The experimental results have demonstrated that in the epithelium‐stroma classification, the proposed method can achieve better performance than the commonly used deep learning methods and some existing deep domain adaptation methods. Therefore, the proposed method can be considered as a better option for the real‐world applications of histopathological image analysis because there is no requirement for recollection of large‐scale labeled data for every specified domain.