Histopathological Image Recognition with Domain Knowledge Based Deep Features

  • Gang Zhang
  • Ming Xiao
  • Yong-hui HuangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10956)


Automatic recognition of histopathological image plays an important role in building computer-aid diagnosis system. Traditionally hand-craft features are widely used for representing histopathological images when building recognition models. Currently with the development of deep learning algorithms, deep features obtained directly from pre-trained networks at less costs show that they perform better than traditional ones. However, most recent work adopts common pre-trained networks for feature extraction and train a classifier with domain knowledge which generates a gap between the common extracted features and the application domain. To fill the gap and improve the performance of the recognition model, in this paper we propose a deep model for histopathological image feature representation in a supervised-learning manner. The proposed model is constructed based on some pre-trained convolutional neural networks. After supervised learning, the feature learning network captures most domain knowledge. The proposed model is evaluated on two histopathological image datasets and the results show that the proposed model is superior to current state-of-the-art models.


Histopathological image recognition Deep learning Convolutional neural network Feature extraction Ensemble learning 



This work is supported by the National Natural Science Foundation of China (No. 81373883, 81573827), the Science and Technology Planning Project of Guangdong Province (No. 2016A030310340), the Special Fund of Cultivation of Technology Innovation for University Students (No. pdjh2016b0150), the College Student Career and Innovation Training Plan Project of Guangdong Province (yj201611845593, yj201611845074, yj201611845075, yj201611845366), the Higher Education Research Funding of Guangdong University of Technology (No. 2016GJ12) and the 2015 Research Project of Guangdong Education Evaluation Association (No. G-11).


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of AutomationGuangdong University of TechnologyGuangzhouChina

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