Abstract
Recently, a great development in image recognition has been achieved, especially by the availability of large and annotated databases and the application of Deep Learning on these data. Convolutional Neural Networks (CNN’s) can be used to enable the extraction of highly representative features among the network layers filtering, selecting and using these features in the last fully connected layers for pattern classification. However, CNN training for automatic medical image classification still provides a challenge due to the lack of large and publicly available annotated databases. In this work, we evaluate and analyze the use of CNN’s as a general feature descriptor doing transfer learning to generate “off-the-shelf” CNN’s features for the colonic polyp classification task. The good results obtained by off-the-shelf CNN’s features in many different databases suggest that features learned from CNN with natural images can be highly relevant for colonic polyp classification.
E. Ribeiro—This research was partially supported by CNPq-Brazil for Eduardo Ribeiro under grant No. 00736/2014-0.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ameling, S., Wirth, S., Paulus, D., Lacey, G., Vilarino, F.: Texture-based polyp detection in colonoscopy. In: Meinzer, H.-P., Deserno, T.M., Handels, H., Tolxdorff, T. (eds.) Bildverarbeitung für die Medizin 2009. Informatik aktuell, pp. 346–350. Springer, Heidelberg (2009)
Bar, Y., Diamant, I., Wolf, L., Lieberman, S., Konen, E., Greenspan, H.: Chest pathology detection using deep learning with non-medical training. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp. 294–297, April 2015
Bernal, J., Schez, J., Vilario, F.: Towards automatic polyp detection with a polyp appearance model. Pattern Recognit. 45(9), 3166–3182 (2012). Best Papers of Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2011)
Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. In: British Machine Vision Conference, BMVC 2014, Nottingham, 1–5 September 2014
Dong, Y., Tao, D., Li, X., Ma, J., Pu, J.: Texture classification and retrieval using shearlets and linear regression. IEEE Trans. Cybern. 45(3), 358–369 (2015)
Ribeiro E., Uhl, A., Häfner, M.: Colonic polyp classification with convolutional neural networks. In: 2016 29th International Symposium on Computer-Based Medical Systems (CBMS), June 2016
Ganz, M., Yang, X., Slabaugh, G.: Automatic segmentation of polyps in colonoscopic narrow-band imaging data. IEEE Trans. Biomed. Eng. 59(8), 2144–2151 (2012)
Ginneken, B., Setio, A., Jacobs, C., Ciompi, F.: Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans. In: 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, Brooklyn, 16–19 April 2015, pp. 286–289 (2015)
Häfner, M., Kwitt, R., Uhl, A., Gangl, A., Wrba, F., Vécsei, A.: Feature extraction from multi-directional multi-resolution image transformations for the classification of zoom-endoscopy images. Pattern Anal. Appl. 12(4), 407–413 (2009)
Häfner, M., Liedlgruber, M., Maimone, S., Uhl, A., Vécsei, A., Wrba, F.: Evaluation of cross-validation protocols for the classification of endoscopic images of colonic polyps. In: 2012 25th International Symposium on Computer-Based Medical Systems (CBMS), pp. 1–6, June 2012
Häfner, M., Liedlgruber, M., Uhl, A., Vécsei, A., Wrba, F.: Color treatment in endoscopic image classification using multi-scale local color vector patterns. Med. Image Anal. 16(1), 75–86 (2012)
Häfner, M., Liedlgruber, M., Uhl, A., Vécsei, A., Wrba, F.: Delaunay triangulation-based pit density estimation for the classification of polyps in high-magnification chromo-colonoscopy. Comput. Methods Programs Biomed. 107(3), 565–581 (2012)
Häfner, M., Tamaki, T., Tanaka, S., Uhl, A., Wimmer, G., Yoshida, S.: Local fractal dimension based approaches for colonic polyp classification. Med. Image Anal. 26(1), 92–107 (2015)
Häfner, M., Uhl, A., Wimmer, G.: A novel shape feature descriptor for the classification of polyps in HD colonoscopy. In: Menze, B., Langs, G., Montillo, A., Kelm, M., Müller, H., Tu, Z. (eds.) MCV 2013. LNCS, vol. 8331, pp. 205–213. Springer, Heidelberg (2014). doi:10.1007/978-3-319-05530-5_20
Shin, H., Roth, H., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., Summers, R.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. CoRR, abs/1602.03409 (2016)
Alex K., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105. Curran Associates Inc. (2012)
Kato, S., Fu, K.I., Sano, Y., Fujii, T., Saito, Y., Matsuda, T., Koba, I., Yoshida, S., Fujimori, T.: Magnifying colonoscopy as a non-biopsy technique for differential diagnosis of non-neoplastic and neoplastic lesions. World J. Gastroenterol. 12(9), 1416–1420 (2006)
Kudo, S., Hirota, S., Nakajima, T.: Colorectal tumours and pit pattern. J. Clin. Pathol. 10, 880–885 (1994)
McNemar, Q.: Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12(2), 153–157 (1947)
Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, 23–28 June 2014, pp. 1717–1724 (2014)
Sun, Y.P., Sargent, D., Spofford, I., Vosburgh, K.G., A-Rahim, Y.: A colon video analysis framework for polyp detection. IEEE Trans. Biomed. Eng. 59(5), 1408–1418 (2012)
Park, S.Y., Sargent, D.: Colonoscopic polyp detection using convolutional neural networks. In: Proceedings of SPIE, vol. 9785, p. 978528 (2016)
Razavian, A., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR Workshops 2014, Columbus, 23–28 June 2014, pp. 512–519 (2014)
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: integrated recognition, localization and detection using convolutional networks. CoRR, abs/1312.6229 (2013)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556 (2014)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Computer Vision and Pattern Recognition (CVPR) (2015)
Tajbakhsh, N., Gurudu, S.R., Liang, J.: Automatic polyp detection in colonoscopy videos using an ensemble of convolutional neural networks. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp. 79–83, April 2015
Tajbakhsh, N., Gurudu, S.R., Liang, J.: Automated polyp detection in colonoscopy videos using shape and context information. IEEE Trans. Med. Imaging 35(2), 630–644 (2016)
Vedaldi, A., Lenc, K.: Matconvnet - convolutional neural networks for MATLAB. CoRR, abs/1412.4564 (2014)
Yi, W., Tavanapong, W., Wong, J., Oh, J., de Groen, P.C.: Part-based multiderivative edge cross-sectional profiles for polyp detection in colonoscopy. IEEE J. Biomed. Health Inform. 18(4), 1379–1389 (2014)
Wang, Y., Tavanapong, W., Wong, J., Oh, J.H., de Groen, P.C.: Polyp-alert: near real-time feedback during colonoscopy. Comput. Methods Programs Biomed. 120(3), 164–179 (2015)
Wimmer, G., Tamaki, T., Tischendorf, J.J.W., Häfner, M., Yoshida, S., Tanaka, S., Uhl, A.: Directional wavelet based features for colonic polyp classification. Med. Image Anal. 31, 16–36 (2016)
Zhu, R., Zhang, R., Xue, D.: Lesion detection of endoscopy images based on convolutional neural network features. In: 2015 8th International Congress on Image and Signal Processing (CISP), pp. 372–376, October 2015
Zou, Y., Li, L., Wang, Y., Yu, J., Li, Y., Deng, W.J.: Classifying digestive organs in wireless capsule endoscopy images based on deep convolutional neural network. In: 2015 IEEE International Conference on Digital Signal Processing (DSP), pp. 1274–1278, July 2015
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Ribeiro, E., Uhl, A., Wimmer, G., Häfner, M. (2017). Transfer Learning for Colonic Polyp Classification Using Off-the-Shelf CNN Features. In: Peters, T., et al. Computer-Assisted and Robotic Endoscopy. CARE 2016. Lecture Notes in Computer Science(), vol 10170. Springer, Cham. https://doi.org/10.1007/978-3-319-54057-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-54057-3_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-54056-6
Online ISBN: 978-3-319-54057-3
eBook Packages: Computer ScienceComputer Science (R0)