RIIS-DenseNet: Rotation-Invariant and Image Similarity Constrained Densely Connected Convolutional Network for Polyp Detection

  • Yixuan YuanEmail author
  • Wenjian Qin
  • Bulat Ibragimov
  • Bin Han
  • Lei Xing
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)


Colorectal cancer is the leading cause of cancer-related deaths. Most colorectal cancers are believed to arise from benign adenomatous polyps. Automatic methods for polyp detection with Wireless Capsule Endoscopy (WCE) images are desirable, but the results of current approaches are limited due to the problems of object rotation and high intra-class variability. To address these problems, we propose a rotation invariant and image similarity constrained Densely Connected Convolutional Network (RIIS-DenseNet) model. We first introduce Densely Connected Convolutional Network (DenseNet), which enables the maximum information flow among layers by a densely connected mechanism, to provide end-to-end polyp detection workflow. The rotation-invariant regularization constraint is then introduced to explicitly enforce learned features of the training samples and the corresponding rotation versions to be mapped close to each other. The image similarity constraint is further proposed by imposing the image category information on the features to maintain small intra-class scatter. Our method achieves an inspiring accuracy 95.62% for polyp detection. Extensive experiments on the WCE dataset show that our method has superior performance compared with state-of-the-art methods.


  1. 1.
    Gueye, L., Yildirim-Yayilgan, S., Cheikh, F.A., Balasingham, I.: Automatic detection of colonoscopic anomalies using capsule endoscopy. In: IEEE ICIP, pp. 1061–1064 (2015)Google Scholar
  2. 2.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE: CVPR, pp. 770–778 (2016)Google Scholar
  3. 3.
    Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. In: CVPR, pp. 4700–4708. IEEE (2017)Google Scholar
  4. 4.
    Iddan, G., Meron, G., Glukhovsky, A., Swain, P.: Wireless capsule endoscopy. Nature 405, 417 (2000)CrossRefGoogle Scholar
  5. 5.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1097–1105 (2012)Google Scholar
  6. 6.
    Seguí, S., et al.: Generic feature learning for wireless capsule endoscopy analysis. Comput. Biol. Med. 79, 163–172 (2016)CrossRefGoogle Scholar
  7. 7.
    Shin, Y., Balasingham, I.: Comparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification. In: IEEE EMBC, pp. 3277–3280 (2017)Google Scholar
  8. 8.
    American Cancer Society: Key statistics for colorectal cancer. Accessed 15 Jan 2018
  9. 9.
    Tajbakhsh, N., Gurudu, S.R., Liang, J.: Automatic polyp detection in colonoscopy videos using an ensemble of convolutional neural networks. In: IEEE ISBI, pp. 79–83 (2015)Google Scholar
  10. 10.
    Yu, L., Chen, H., Dou, Q., Qin, J., Heng, P.A.: Integrating online and offline three-dimensaional deep learning for automated polyp detection in colonoscopy videos. IEEE J. Biomed. Health Inform. 21(1), 65–75 (2017)CrossRefGoogle Scholar
  11. 11.
    Yuan, Y., Meng, M.Q.H.: Deep learning for polyp recognition in wireless capsule endoscopy images. Med. Phys. 44(4), 1379–1389 (2017)CrossRefGoogle Scholar
  12. 12.
    Zhang, R., et al.: Automatic detection and classification of colorectal polyps by transferring low-level cnn features from nonmedical domain. IEEE J. Biomed. Health Inform. 21(1), 41–47 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Electronic EngineeringCity Univeristy of Hong KongHong KongChina
  2. 2.Department of Radiation OncologyStanford UniversityStanfordUSA
  3. 3.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina

Personalised recommendations