Cell Encoding for Histopathology Image Classification

  • Xiaoshuang Shi
  • Fuyong Xing
  • Yuanpu Xie
  • Hai Su
  • Lin YangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)


Although many image analysis algorithms can achieve good performance with sufficient number of labeled images, manually labeling images by pathologists is time consuming and expensive. Meanwhile, with the development of cell detection and segmentation techniques, it is possible to classify pathology images by using cell-level information, which is crucial to grade different diseases; however, it is still very challenging to efficiently conduct cell analysis on large-scale image databases since one image often contains a large number of cells. To address these issues, in this paper, we present a novel cell-based framework that requires only a few labeled images to classify large-scale pathology ones. Specifically, we encode each cell into a set of binary codes to generate image representation using a semi-supervised hashing model, which can take advantage of both labeled and unlabeled cells. Thereafter, we map all the binary codes in one whole image into a single histogram vector and then learn a support vector machine for image classification. The proposed framework is validated on one large-scale lung cancer image dataset with two types of diseases, and it can achieve 87.88% classification accuracy on 800 test images using only 5 labeled images of each disease.


Histopathological Image Classification Lung Cancer Imaging Pathology Image Image Labeling Semi-supervised Hashing (SSH) 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    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, vol. 5651, pp. 126–135. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-02976-9_17CrossRefGoogle Scholar
  2. 2.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, pp. 886–893 (2005)Google Scholar
  3. 3.
    Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9(Aug), 1871–1874 (2008)zbMATHGoogle Scholar
  4. 4.
    Hou, L., Samaras, D., Kurc, T.M., Gao, Y., Davis, J.E., Saltz, J.H.: Patch-based convolutional neural network for whole slide tissue image classification. In: CVPR, pp. 2424–2433 (2016)Google Scholar
  5. 5.
    National Cancer Institute: The Cancer Genome Atals (2013).
  6. 6.
    Jiang, M., Zhang, S., Huang, J., Yang, L., Metaxas, D.N.: Joint kernel-based supervised hashing for scalable histopathological image analysis. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 366–373. Springer, Cham (2015). doi: 10.1007/978-3-319-24574-4_44CrossRefGoogle Scholar
  7. 7.
    Liu, W., Wang, J., Ji, R., Jiang, Y., Chang, S.: Supervised hashing with kernels. In: CVPR, pp. 2074–2081 (2012)Google Scholar
  8. 8.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  9. 9.
    Mesbah, S., Conjeti, S., Kumaraswamy, A., Rautenberg, P., Navab, N., Katouzian, A.: Hashing forests for morphological search and retrieval in neuroscientific image databases. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9350, pp. 135–143. Springer, Cham (2015). doi: 10.1007/978-3-319-24571-3_17CrossRefGoogle Scholar
  10. 10.
    Petushi, S., Garcia, F.U., Haber, M.M., Katsinis, C., Tozeren, A.: Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer. Bio. Med. Comput. Med. Imag. 6(1), 1 (2006)Google Scholar
  11. 11.
    Shah, A., Conjeti, S., Navab, N., Katouzian, A.: Deeply learnt hashing forests for content based image retrieval in prostate MR images. In: SPIE Medical Imaging, p. 978414 (2016)Google Scholar
  12. 12.
    Shen, F., Shen, C., Liu, W., Shen, H.: Supervised discrete hashing. In: CVPR, pp. 37–45 (2015)Google Scholar
  13. 13.
    Shi, X., Xing, F., Cai, J., Zhang, Z., Xie, Y., Yang, L.: Kernel-based supervised discrete hashing for image retrieval. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 419–433. Springer, Cham (2016). doi: 10.1007/978-3-319-46478-7_26CrossRefGoogle Scholar
  14. 14.
    Shi, X., Xing, F., Xu, K., Sapkota, M., Yang, L.: Asymmetric discrete graph hashing. In: AAAI (2017)Google Scholar
  15. 15.
    Wang, J., Kumar, S., Chang, S.: Semi-supervised hashing for large-scale search. IEEE Trans. Pattern Anal. Mach. Intell. 34(12), 2393–2406 (2012)CrossRefGoogle Scholar
  16. 16.
    Xing, F., Su, H., Neltner, J., Yang, L.: Automatic Ki-67 counting using robust cell detection and online dictionary learning. IEEE Trans. Bio. Med. Eng. 61(3), 859–870 (2014)CrossRefGoogle Scholar
  17. 17.
    Yang, L., Chen, W., Meer, P., Salaru, G., Feldman, M.D., Foran, D.J.: High throughput analysis of breast cancer specimens on the grid. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007. LNCS, vol. 4791, pp. 617–625. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-75757-3_75CrossRefGoogle Scholar
  18. 18.
    Zhang, S., Metaxas, D.: Large-scale medical image analytics: recent methodologies, applications and future directions. Med. Imag. Anal. 33, 98–101 (2016)CrossRefGoogle Scholar
  19. 19.
    Zhang, X., Su, H., Yang, L., Zhang, S.: Fine-grained histopathological image analysis via robust segmentation and large-scale retrieval. In: CVPR, pp. 5361–5368 (2015)Google Scholar
  20. 20.
    Zhang, X., Su, H., Yang, L., Zhang, S.: Weighted hashing with multiple cues for cell-level analysis of histopathological images. In: Ourselin, S., Alexander, D.C., Westin, C.-F., Cardoso, M.J. (eds.) IPMI 2015. LNCS, vol. 9123, pp. 303–314. Springer, Cham (2015). doi: 10.1007/978-3-319-19992-4_23CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Xiaoshuang Shi
    • 1
  • Fuyong Xing
    • 1
    • 2
  • Yuanpu Xie
    • 1
  • Hai Su
    • 1
  • Lin Yang
    • 1
    • 2
    Email author
  1. 1.J. Crayton Pruitt Family Department of Biomedical EngineeringUniversity of FloridaGainesvilleUSA
  2. 2.Department of Electrical and Computer EngineeringUniversity of FloridaGainesvilleUSA

Personalised recommendations