Skip to main content
SpringerLink
Log in

Medical Image Tagging by Deep Learning and Retrieval

  • Conference paper
  • First Online:
Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12260))

Abstract

Radiologists and other qualified physicians need to examine and interpret large numbers of medical images daily. Systems that would help them spot and report abnormalities in medical images could speed up diagnostic workflows. Systems that would help exploit past diagnoses made by highly skilled physicians could also benefit their more junior colleagues. A task that systems can perform towards this end is medical image classification, which assigns medical concepts to images. This task, called Concept Detection, was part of the ImageCLEF 2019 competition. We describe the methods we implemented and submitted to the Concept Detection 2019 task, where we achieved the best performance with a deep learning method we call ConceptCXN. We also show that retrieval-based methods can perform very well in this task, when combined with deep learning image encoders. Finally, we report additional post-competition experiments we performed to shed more light on the performance of our best systems. Our systems can be installed through PyPi as part of the BioCaption package.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.imageclef.org/2019/medical/caption/.

  2. 2.

    http://nlp.cs.aueb.gr/.

  3. 3.

    https://www.ncbi.nlm.nih.gov/pmc/.

  4. 4.

    https://www.nlm.nih.gov/research/umls/.

  5. 5.

    We used the implementation of https://keras.io/applications/#densenet.

  6. 6.

    https://keras.io/.

  7. 7.

    https://keras.io/applications/#vgg19.

  8. 8.

    A similar observation was made in [7].

References

  1. Berlin, L.: Accuracy of diagnostic procedures: has it improved over the past five decades? Am. J. Roentgenol. 188(5), 1173–1178 (2007)

    Article  Google Scholar 

  2. Bien, N., et al.: Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet. PLoS Med. 15(11), 1–19 (2018)

    Article  Google Scholar 

  3. Chokshi, F.H., Hughes, D.R., Wang, J.M., Mullins, M.E., Hawkins Jr., C.M., Duszak, R.: Diagnostic radiology resident and fellow workloads: a 12-year longitudinal trend analysis using national medicare aggregate claims data. J. Am. Coll. Radiol. 12(7), 664–669 (2015)

    Article  Google Scholar 

  4. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, Miami Beach, FL, USA, pp. 248–255 (2009)

    Google Scholar 

  5. Eickhoff, C., Schwall, I., de Herrera, A.G.S., Müller, H.: Overview of ImageCLEFcaption 2017 - the image caption prediction and concept extraction tasks to understand biomedical images. In: CLEF2017 Working Notes. CEUR Workshop Proceedings. CEUR-WS.org, Dublin (2017). http://ceur-ws.org

  6. Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017)

    Article  Google Scholar 

  7. Gonçalves, A.J., Pinho, E., Costa, C.: Informative and intriguing visual features: UA.PT Bioinformatics in ImageCLEF Caption 2019. In: CLEF2019 Working Notes. CEUR Workshop Proceedings, Lugano, Switzerland (2019)

    Google Scholar 

  8. Gong, Y., Jia, Y., Leung, T., Toshev, A., Ioffe, S.: Deep convolutional ranking for multilabel image annotation. In: International Conference on Learning Representations (2014)

    Google Scholar 

  9. de Herrera, A.G.S., Eickhoff, C., Andrearczyk, V., Müller, H.: Overview of the ImageCLEF 2018 caption prediction tasks. In: CLEF2018 Working Notes. CEUR Workshop Proceedings, CEUR-WS.org, Avignon (2018). http://ceur-ws.org

  10. Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp. 4700–4708 (2017)

    Google Scholar 

  11. Ionescu, B., et al.: ImageCLEF 2019: multimedia retrieval in medicine, lifelogging, security and nature. In: Crestani, F., et al. (eds.) CLEF 2019. LNCS, vol. 11696, pp. 358–386. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28577-7_28

    Chapter  Google Scholar 

  12. Irvin, J., et al.: CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. arXiv:1901.07031 (2019)

  13. Islam, M.T., Aowal, M.A., Minhaz, A.T., Ashraf, K.: Abnormality detection and localization in chest x-rays using deep convolutional neural networks. arXiv:1705.09850 (2017)

  14. Jing, B., Xie, P., Xing, E.: On the automatic generation of medical imaging reports. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Long Papers), Melbourne, Australia, pp. 2577–2586 (2018)

    Google Scholar 

  15. Johnson, A.E., et al..: MIMIC-CXR: a large publicly available database of labeled chest radiographs. arXiv:1901.07042 (2019)

  16. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980 (2014)

  17. Kougia, V., Pavlopoulos, J., Androutsopoulos, I.: A survey on biomedical image captioning. In: Workshop on Shortcomings in Vision and Language of the Annual Conference of the North American Chapter of the Association for Computational Linguistics, Minneapolis, MN, USA, pp. 26–36 (2019)

    Google Scholar 

  18. Kougia, V., Pavlopoulos, J., Androutsopoulos, I.: AUEB NLP group at ImageCLEFmed caption 2019. In: CLEF2019 Working Notes. CEUR Workshop Proceedings, Lugano, Switzerland (2019)

    Google Scholar 

  19. Mahajan, D., et al.: Exploring the limits of weakly supervised pretraining. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 185–201. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01216-8_12

    Chapter  Google Scholar 

  20. Pelka, O., Friedrich, C.M., de Herrera, A.G.S., Müller, H.: Overview of the ImageCLEFmed 2019 concept prediction task. In: CLEF2019 Working Notes. CEUR Workshop Proceedings, vol. ISSN 1613–0073. CEUR-WS.org, Lugano (2019). http://ceur-ws.org/Vol-2380/

  21. Pelka, O., Koitka, S., Rückert, J., Nensa, F., Friedrich, C.M.: Radiology objects in COntext (ROCO): a multimodal image dataset. In: Stoyanov, D., et al. (eds.) LABELS/CVII/STENT -2018. LNCS, vol. 11043, pp. 180–189. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01364-6_20

    Chapter  Google Scholar 

  22. Pinho, E., Costa, C.: Feature learning with adversarial networks for concept detection in medical images: UA.PT bioinformatics at ImageCLEF 2018. In: CLEF2018 Working Notes. CEUR Workshop Proceedings, Avignon, France (2018)

    Google Scholar 

  23. Rajpurkar, P., et al.: MURA: large dataset for abnormality detection in musculoskeletal radiographs. arXiv:1712.06957 (2017)

  24. Rajpurkar, P., Irvin, J., Ball, R.L., Zhu, K., Yang, B., Mehta, H., et al.: Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLOS Med. 15(11), 1–17 (2018)

    Article  Google Scholar 

  25. Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., et al.: CheXNet: radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv:1711.05225 (2017)

  26. Rimmer, A.: Radiologist shortage leaves patient care at risk, warns royal college. Br. Med. J. 359 (2017)

    Google Scholar 

  27. Rosman, D.A., et al.: Imaging in the land of 1000 hills: Rwanda radiology country report. J. Glob. Radiol. 1(1), 5 (2015)

    Google Scholar 

  28. Shin, H.C., Roberts, K., Lu, L., Demner-Fushman, D., Yao, J., Summers, R.M.: Learning to read chest x-rays: recurrent neural cascade model for automated image annotation. In: IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp. 2497–2506 (2016)

    Google Scholar 

  29. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)

  30. Soldaini, L., Goharian, N.: QuickUMLS: a fast, unsupervised approach for medical concept extraction. In: MedIR Workshop (2016)

    Google Scholar 

  31. Valavanis, L., Stathopoulos, S.: IPL at ImageCLEF 2017 concept detection task. In: CLEF CEUR Workshop, Dublin, Ireland (2017)

    Google Scholar 

  32. Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: ChestX-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp. 2097–2106 (2017)

    Google Scholar 

  33. Yin, C., et al.: Automatic generation of medical imaging diagnostic report with hierarchical recurrent neural network. In: IEEE International Conference on Data Mining (ICDM), Beijing, China, pp. 728–737 (2019)

    Google Scholar 

  34. Zhang, Y., Wang, X., Guo, Z., Li, J.: ImageSem at ImageCLEF 2018 caption task: image retrieval and transfer learning. In: CLEF2018 Working Notes. CEUR Workshop Proceedings, Avignon, France (2018)

    Google Scholar 

Download references

Acknowledgements

We thank Vasilis Karatzas for his assistance with the post-competition experiments.

Author information

Authors and Affiliations

  1. Department of Informatics, Athens University of Economics and Business, Athens, Greece

    Vasiliki Kougia, John Pavlopoulos & Ion Androutsopoulos

  2. Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden

    Vasiliki Kougia & John Pavlopoulos

Authors
  1. Vasiliki Kougia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John Pavlopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ion Androutsopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to John Pavlopoulos .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece

    Avi Arampatzis

  2. University of Amsterdam, Amsterdam, The Netherlands

    Evangelos Kanoulas

  3. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Theodora Tsikrika

  4. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Stefanos Vrochidis

  5. Faculty of Library, Information and Media Science, University of Tsukuba, Ibaraki, Japan

    Hideo Joho

  6. Department of Computer Science, University of Copenhagen, Copenhagen, Denmark

    Christina Lioma

  7. Brown University, Providence, RI, USA

    Carsten Eickhoff

  8. LIMSI-CNRS, Orsay, France

    Aurélie Névéol

  9. Department of Information Engineering, University of Padova, Padua, Italy

    Linda Cappellato

  10. Department of Information Engineering, University of Padova, Padua, Italy

    Nicola Ferro

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kougia, V., Pavlopoulos, J., Androutsopoulos, I. (2020). Medical Image Tagging by Deep Learning and Retrieval. In: Arampatzis, A., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2020. Lecture Notes in Computer Science(), vol 12260. Springer, Cham. https://doi.org/10.1007/978-3-030-58219-7_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58219-7_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58218-0

  • Online ISBN: 978-3-030-58219-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation