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Optimized Convolutional Neural Network Ensembles for Medical Subfigure Classification

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Book cover Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10456))

Abstract

Automatic classification systems are required to support medical literature databases like PubMedCentral, which allow an easy access to millions of articles. FHDO Biomedical Computer Science Group (BCSG) participated at the ImageCLEF 2016 Subfigure Classification Task to improve existing approaches for classifying figures from medical literature. In this work, a data analysis is conducted in order to improve image preprocessing for deep learning approaches. Evaluations on the dataset show better ensemble classification accuracies using only visual information with an optimized training, in comparison to the mixed feature approaches of BCSG at ImageCLEF 2016. Additionally, a self-training approach is investigated to generate more labeled data in the medical domain.

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Notes

  1. 1.

    https://www.ncbi.nlm.nih.gov/pmc/ (last access: 19.04.2017).

  2. 2.

    https://openi.nlm.nih.gov/ (last access: 19.04.2017).

  3. 3.

    https://ceb.nlm.nih.gov/ridem/iti.html (last access: 24.04.2017).

  4. 4.

    https://github.com/NVIDIA/DIGITS (last access: 20.04.2017).

  5. 5.

    https://github.com/tensorflow/models/blob/master/slim (last access: 09.03.2017).

  6. 6.

    https://www.tensorflow.org (last access: 09.03.2017).

  7. 7.

    https://github.com/razorx89/imageclef-med-2016-follow-up-research (last access: 03.05.2017).

  8. 8.

    https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/ (last access: 20.04.2017).

References

  1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I.J., Harp, A., Irving, G., Isard, M., Jia, Y., Józefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D.G., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P.A., Vanhoucke, V., Vasudevan, V., Viégas, F.B., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: large-scale machine learning on heterogeneous distributed systems. CoRR abs/1603.04467 (2016). http://arxiv.org/abs/1603.04467

  2. Csurka, G. (ed.): Domain Adaption in Computer Vision Applications. Advances in Computer Vision and Pattern Recognition, 1 edn. Springer International Publishing (2017). http://www.springer.com/de/book/9783319583464

  3. Demner-Fushman, D., Antani, S., Simpson, M., Thoma, G.R.: Design and development of a multimodal biomedical information retrieval system. J. Comput. Sci. Eng. 6(2), 168–177 (2012)

    Article  Google Scholar 

  4. Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: DeCAF: a deep convolutional activation feature for generic visual recognition. In: Jebara, T., Xing, E.P. (eds.) Proceedings of the 31st International Conference on Machine Learning (ICML 2014), pp. 647–655, JMLR Workshop and Conference Proceedings (2014)

    Google Scholar 

  5. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016

    Google Scholar 

  6. García Seco de Herrera, A., Kalpathy-Cramer, J., Demner Fushman, D., Antani, S., Müller, H.: Overview of the ImageCLEF 2013 medical tasks. In: Working Notes of CLEF 2013 (Cross Language Evaluation Forum), CEUR Workshop Proceedings, vol. 1179, September 2013

    Google Scholar 

  7. García Seco de Herrera, A., Schaer, R., Antani, S., Müller, H.: Using crowdsourcing for multi-label biomedical compound figure annotation. In: Carneiro, G., et al. (eds.) LABELS/DLMIA -2016. LNCS, vol. 10008, pp. 228–237. Springer, Cham (2016). doi:10.1007/978-3-319-46976-8_24

  8. García Seco de Herrera, A., Schaer, R., Bromuri, S., Müller, H.: Overview of the ImageCLEF 2016 medical task. In: CLEF2016 Working Notes, CEUR Workshop Proceedings, CEUR-WS.org, Évora, Portugal (2016). http://ceur-ws.org

  9. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia (MM 2014), NY, USA, pp. 675–678. ACM, New York (2014)

    Google Scholar 

  10. Koitka, S., Friedrich, C.M.: Traditional feature engineering and deep learning approaches at medical classification task of ImageCLEF 2016. In: Working Notes of CLEF 2016 - Conference and Labs of the Evaluation forum, Èvora, Portugal, 5–8 September, 2016. CEUR-WS Proceedings Notes, vol. 1609, pp. 304–317, July 2016

    Google Scholar 

  11. Kumar, A., Kim, J., Lyndon, D., Fulham, M., Feng, D.: An ensemble of fine-tuned convolutional neural networks for medical image classification. IEEE J. Biomed. Health Inform. 21(1), 31–40 (2017)

    Article  Google Scholar 

  12. Lê Cao, K.A., Boitard, S., Besse, P.: Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems. BMC Bioinform. 12(1), 253 (2011)

    Article  Google Scholar 

  13. Pelka, O., Friedrich, C.M.: FHDO biomedical computer science group at medical classification task of imageclef 2015. In: Working Notes of CLEF 2015 - Conference and Labs of the Evaluation forum, Toulouse, France, 8–11 September 2015. CEUR-WS Proceedings Notes, vol. 1391 (2015)

    Google Scholar 

  14. Pelka, O., Friedrich, C.M.: Modality prediction of biomedical literature images using multimodal feature representation. GMS Med. Inform. Biometry Epidemiol. (MIBE) 12(1), Doc4 (2016)

    Google Scholar 

  15. Personnaz, L., Guyon, I., Dreyfus, G.: Collective computational properties of neural networks: new learning mechanisms. Phys. Rev. A (General Physics) 34(5), 4217–4228 (1986)

    Article  MathSciNet  Google Scholar 

  16. Ried, K., Frank, O.R., Stocks, N.P., Fakler, P., Sullivan, T.: Effect of garlic on blood pressure: a systematic review and meta-analysis. BMC Cardiovasc. Disorders 8(1), 13 (2008)

    Article  Google Scholar 

  17. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  18. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, Inception-ResNet and the impact of residual connections on learning. In: ICLR 2016 Workshop (2016)

    Google Scholar 

  19. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015

    Google Scholar 

  20. Valavanis, L., Stathopoulos, S., Kalamboukis, T.: IPL at CLEF 2016 medical task. In: Working Notes of CLEF 2016 - Conference and Labs of the Evaluation forum, Èvora, Portugal, 5–8 September 2016. CEUR-WS Proceedings Notes, vol. 1609, pp. 413–420 (2016). http://ceur-ws.org/Vol-1609/

  21. Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 27. pp. 3320–3328. Curran Associates, Inc. (2014)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, University of Applied Sciences and Arts Dortmund (FHDO), Emil-Figge-Strasse 42, 44227, Dortmund, Germany

    Sven Koitka & Christoph M. Friedrich

  2. Department of Computer Science, TU Dortmund University, Otto-Hahn-Str. 14, 44227, Dortmund, Germany

    Sven Koitka

Authors
  1. Sven Koitka
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  2. Christoph M. Friedrich
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Corresponding authors

Correspondence to Sven Koitka or Christoph M. Friedrich .

Editor information

Editors and Affiliations

  1. Dublin City University, Dublin, Ireland

    Gareth J.F. Jones

  2. Trinity College Dublin, Dublin, Ireland

    Séamus Lawless

  3. National University of Distance Education, Madrid, Spain

    Julio Gonzalo

  4. Dublin City University, Dublin, Ireland

    Liadh Kelly

  5. Université Grenoble Alpes, Grenoble, France

    Lorraine Goeuriot

  6. University of Hildesheim, Hildesheim, Germany

    Thomas Mandl

  7. University of Padua, Padua, Italy

    Linda Cappellato

  8. University of Padua, Padua, Italy

    Nicola Ferro

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Koitka, S., Friedrich, C.M. (2017). Optimized Convolutional Neural Network Ensembles for Medical Subfigure Classification. In: Jones, G., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2017. Lecture Notes in Computer Science(), vol 10456. Springer, Cham. https://doi.org/10.1007/978-3-319-65813-1_5

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  • DOI: https://doi.org/10.1007/978-3-319-65813-1_5

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  • Online ISBN: 978-3-319-65813-1

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