Advertisement

Medical Image Tagging by Deep Learning and Retrieval

Conference paper
  • 519 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Medical images Concept detection Image retrieval Multi-label classification Image captioning Machine learning Deep learning 

Notes

Acknowledgements

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

References

  1. 1.
    Berlin, L.: Accuracy of diagnostic procedures: has it improved over the past five decades? Am. J. Roentgenol. 188(5), 1173–1178 (2007)CrossRefGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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. 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. 6.
    Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017)CrossRefGoogle Scholar
  7. 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. 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. 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. 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. 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_28CrossRefGoogle Scholar
  12. 12.
    Irvin, J., et al.: CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. arXiv:1901.07031 (2019)
  13. 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. 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. 15.
    Johnson, A.E., et al..: MIMIC-CXR: a large publicly available database of labeled chest radiographs. arXiv:1901.07042 (2019)
  16. 16.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980 (2014)
  17. 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. 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. 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_12CrossRefGoogle Scholar
  20. 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. 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_20CrossRefGoogle Scholar
  22. 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. 23.
    Rajpurkar, P., et al.: MURA: large dataset for abnormality detection in musculoskeletal radiographs. arXiv:1712.06957 (2017)
  24. 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)CrossRefGoogle Scholar
  25. 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. 26.
    Rimmer, A.: Radiologist shortage leaves patient care at risk, warns royal college. Br. Med. J. 359 (2017)Google Scholar
  27. 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. 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. 29.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)
  30. 30.
    Soldaini, L., Goharian, N.: QuickUMLS: a fast, unsupervised approach for medical concept extraction. In: MedIR Workshop (2016)Google Scholar
  31. 31.
    Valavanis, L., Stathopoulos, S.: IPL at ImageCLEF 2017 concept detection task. In: CLEF CEUR Workshop, Dublin, Ireland (2017)Google Scholar
  32. 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. 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. 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

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of InformaticsAthens University of Economics and BusinessAthensGreece
  2. 2.Department of Computer and Systems SciencesStockholm UniversityStockholmSweden

Personalised recommendations