Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Cover of the book

2017 | OriginalPaper | Chapter

Transfer Learning for Colonic Polyp Classification Using Off-the-Shelf CNN Features

Authors : Eduardo Ribeiro, Andreas Uhl, Georg Wimmer, Michael Häfner

Published in: Computer-Assisted and Robotic Endoscopy

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
next chapter Probe Tracking and Its Application in Automatic Acquisition Using a Trans-Esophageal Ultrasound Robot
Literature
1.
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)CrossRef
2.
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
3.
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)CrossRef
4.
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
5.
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)CrossRef
6.
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
7.
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)CrossRef
8.
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)
9.
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)MathSciNetCrossRef
10.
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
11.
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)CrossRef
12.
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)CrossRef
13.
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)CrossRef
14.
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 CrossRef
15.
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)
16.
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)
17.
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)CrossRef
18.
Kudo, S., Hirota, S., Nakajima, T.: Colorectal tumours and pit pattern. J. Clin. Pathol. 10, 880–885 (1994)CrossRef
19.
McNemar, Q.: Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12(2), 153–157 (1947)CrossRef
20.
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)
21.
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)CrossRef
22.
Park, S.Y., Sargent, D.: Colonoscopic polyp detection using convolutional neural networks. In: Proceedings of SPIE, vol. 9785, p. 978528 (2016)
23.
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)
24.
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)
25.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556 (2014)
26.
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)
27.
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
28.
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)CrossRef
29.
Vedaldi, A., Lenc, K.: Matconvnet - convolutional neural networks for MATLAB. CoRR, abs/1412.4564 (2014)
30.
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)CrossRef
31.
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)CrossRef
32.
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)CrossRef
33.
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
34.
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
Metadata
Title
Transfer Learning for Colonic Polyp Classification Using Off-the-Shelf CNN Features
Authors
Eduardo Ribeiro
Andreas Uhl
Georg Wimmer
Michael Häfner
Copyright Year
2017
Book
Computer-Assisted and Robotic Endoscopy

Print ISBN: 978-3-319-54056-6

Electronic ISBN: 978-3-319-54057-3

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-54057-3_1

Premium Partner

    Image Credits