Skip to main content

Weakly Supervised Universal Fracture Detection in Pelvic X-Rays

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11769)


Hip and pelvic fractures are serious injuries with life-threatening complications. However, diagnostic errors of fractures in pelvic X-rays (PXRs) are very common, driving the demand for computer-aided diagnosis (CAD) solutions. A major challenge lies in the fact that fractures are localized patterns that require localized analyses. Unfortunately, the PXRs residing in hospital picture archiving and communication system do not typically specify region of interests. In this paper, we propose a two-stage hip and pelvic fracture detection method that executes localized fracture classification using weakly supervised ROI mining. The first stage uses a large capacity fully-convolutional network, i.e., deep with high levels of abstraction, in a multiple instance learning setting to automatically mine probable true positive and definite hard negative ROIs from the whole PXR in the training data. The second stage trains a smaller capacity model, i.e., shallower and more generalizable, with the mined ROIs to perform localized analyses to classify fractures. During inference, our method detects hip and pelvic fractures in one pass by chaining the probability outputs of the two stages together. We evaluate our method on \(4\,410\) PXRs, reporting an under the ROC curve value of 0.975, the highest among state-of-the-art fracture detection methods. Moreover, we show that our two-stage approach can perform comparably to human physicians (even outperforming emergency physicians and surgeons), in a preliminary reader study of 23 readers.


  • Fracture classification and localization
  • Pelvic X-ray
  • Weakly supervised detection
  • Cascade two-stage training
  • Image level labels

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-32226-7_51
  • Chapter length: 9 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   99.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-32226-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   129.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.


  1. Badgeley, M.A., Zech, J.R., Oakden-Rayner, L., et al.: Deep learning predicts hip fracture using confounding patient and healthcare variables. arXiv:1811.03695 (2018)

  2. Chellam, W.: Missed subtle fractures on the trauma-meeting digital projector. Injury 47(3), 674–676 (2016)

    CrossRef  Google Scholar 

  3. Chen, H., Miao, S., Xu, D., Hager, G.D., Harrison, A.P.: Deep hierarchical multi-label classification of chest X-ray images. In: MIDL (2019)

    Google Scholar 

  4. Gale, W., Oakden-Rayner, L., Carneiro, G., Bradley, A.P., Palmer, L.J.: Detecting hip fractures with radiologist-level performance using deep neural networks. arXiv:1711.06504 (2017)

  5. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  6. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: IEEE CVPR, pp. 4700–4708 (2017)

    Google Scholar 

  7. Jiménez-Sánchez, A., et al.: Weakly-supervised localization and classification of proximal femur fractures. arXiv:1809.10692 (2018)

  8. Johnell, O., Kanis, J.: An estimate of the worldwide prevalence, mortality and disability associated with hip fracture. Osteoporos. Int. 15(11), 897–902 (2004)

    CrossRef  Google Scholar 

  9. Rajpurkar, P., et al.: CheXNet: radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225 (2017)

  10. Tarrant, S., Hardy, B., Byth, P., Brown, T., Attia, J., Balogh, Z.: Preventable mortality in geriatric hip fracture inpatients. Bone Joint J. 96(9), 1178–1184 (2014)

    CrossRef  Google Scholar 

  11. 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: IEEE CVPR (2017)

    Google Scholar 

  12. Yao, L., Prosky, J., Poblenz, E., Covington, B., Lyman, K.: Weakly supervised medical diagnosis and localization from multiple resolutions. arXiv preprint arXiv:1803.07703 (2018)

  13. Yuille, A.L., Liu, C.: Deep nets: what have they ever done for vision? CoRR abs/1805.04025 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Yirui Wang .

Editor information

Editors and Affiliations

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 11733 KB)

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Wang, Y. et al. (2019). Weakly Supervised Universal Fracture Detection in Pelvic X-Rays. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11769. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32225-0

  • Online ISBN: 978-3-030-32226-7

  • eBook Packages: Computer ScienceComputer Science (R0)