Histopathological Image Recognition with Domain Knowledge Based Deep Features
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.
KeywordsHistopathological 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).
- 2.Wang, C., Shi, J., Zhang, Q., Ying, S.: Histopathological image classification with bilinear convolutional neural networks. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4050–4053, July 2017Google Scholar
- 3.Caicedo, J.C., Cruz, A., Gonzalez, F.A.: Histopathology image classification using bag of features and Kernel functions. In: Combi, C., Shahar, Y., Abu-Hanna, A. (eds.) AIME 2009. LNCS (LNAI), vol. 5651, pp. 126–135. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02976-9_17CrossRefGoogle Scholar
- 6.Zhang, G., Yin, J., Su, X.-y., Huang, Y.-j., Lao, Y.-r., Liang, Z.-h., Ou, S.-x., Zhang, H.-l.: Augmenting multi-instance multilabel learning with sparse Bayesian models for skin biopsy image analysis. Biomed. Res. Int. 2014, 13 (2014). Article ID 305629Google Scholar
- 7.Zhang, G., Hsu, C.-H.R., Lai, H., Zheng, X.: Deep learning based feature representation for automated skin histopathological image annotation. Multimed. Tools Appl., 1–21 (2017)Google Scholar
- 10.Zheng, Y., Jiang, Z., Zhang, H., Xie, F., Ma, Y., Shi, H., Zhao, Y.: Histopathological whole slide image analysis using context-based CBIR. IEEE Trans. Med. Imaging PP(99), 1 (2018)Google Scholar
- 11.Urdal, J., Engan, K., Kvikstad, V., Janssen, E.A.M.: Prognostic prediction of histopathological images by local binary patterns and rusboost. In: 2017 25th European Signal Processing Conference (EUSIPCO), pp. 2349–2353, August 2017Google Scholar
- 12.Spanhol, F.A., Oliveira, L.S., Cavalin, P.R., Petitjean, C., Heutte, L.: Deep features for breast cancer histopathological image classification. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1868–1873, October 2017Google Scholar
- 13.He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)Google Scholar
- 17.Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9, June 2015Google Scholar
- 18.Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25(2), pp. 1097–1105 (2012)Google Scholar