Skip to main content
SpringerLink
Log in

Use of systems with deep learning and machine learning for the detection and classification of malignant vs. benign spinal fractures with MRI: can deep/machine learning help us further for detection and characterization?

  • Commentary
  • Published:
European Radiology Aims and scope Submit manuscript

The Original Article was published on 10 May 2023

The Original Article was published on 26 April 2023

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  1. Liu B, Jin Y, Feng S et al (2023) Benign vs malignant vertebral compression fractures with MRI: a comparison between automatic deep learning network and radiologist’s assessment. Eur Radiol. https://doi.org/10.1007/s00330-023-09713-x

    Article  PubMed  PubMed Central  Google Scholar 

  2. Zhang H, Yuan G, Wang C et al (2023) Differentiation of benign versus malignant indistinguishable vertebral compression fractures by different machine learning with MRI-based radiomic features. Eur Radiol. https://doi.org/10.1007/s00330-023-09678-x

  3. Varoquaux G, Cheplygina V (2022) Machine learning for medical imaging: methodological failures and recommendations for the future. NPJ Digit Med 5:48. https://doi.org/10.1038/s41746-022-00592-y

    Article  PubMed  PubMed Central  Google Scholar 

  4. Selvaraju RR, Cogswell M, Das M, Vedantam R, Parikh D, Batra D (2017) Grad-CAM: visual explanations from deep networks via gradient-based localization. IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017, 618–626, https://doi.org/10.1109/ICCV.2017.74

  5. Wong S, Gatt C, Stamatescu A, McDonnell V (2016) Understanding data augmentation for classification: when to warp? International Conference on Digital Image Computing: Techniques and Applications (DICTA) 2016:1–6. https://doi.org/10.1109/DICTA.2016.779709

    Article  Google Scholar 

  6. Yanase J, Triantaphyllou E (2019) Systematic survey of computer-aided diagnosis in medicine: past and present developments. Expert Systems with Applications 138:112821. https://doi.org/10.1016/j.eswa.2019.112821

    Article  Google Scholar 

  7. Portal-Diaz JA, Lovelle-Enríquez O, Perez-Diaz M, López-Cabrera JD, Reyes-Cardoso O, Orozco-Morales R (2022) New patch-based strategy for covid-19 automatic identification using chest x-ray images. Heal Technol 12:1117–1132. https://doi.org/10.1007/s12553-022-00704-4

    Article  Google Scholar 

  8. Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Lecture Notes in Computer Science 9351. Springer, Cham. https://doi.org/10.1007/978-3-319-24574-4_28

Download references

Author information

Authors and Affiliations

  1. Departamento de Automática, Universidad Central Marta Abreu de Las Villas, Villa Clara, Santa Clara, Cuba

    Marlen Perez-Diaz

Authors
  1. Marlen Perez-Diaz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marlen Perez-Diaz.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This comment refers to two articles: https://doi.org/10.1007/s00330-023-09713-x and https://doi.org/10.1007/s00330-023-09678-x

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Perez-Diaz, M. Use of systems with deep learning and machine learning for the detection and classification of malignant vs. benign spinal fractures with MRI: can deep/machine learning help us further for detection and characterization?. Eur Radiol 33, 5058–5059 (2023). https://doi.org/10.1007/s00330-023-09760-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-023-09760-4

Search

Navigation