Skip to main content

Advertisement

Springer Nature Link
Log in

Automated mammographic mass detection using deformable convolution and multiscale features

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Designing computer-assisted diagnosis (CAD) systems that can precisely identify lesions from mammography images would be useful for clinicians. Considering the morphological variation in breast cancer, it is necessary to extract robust features from the mammogram. Here, we propose a mass detection CAD system that is based on Faster R-CNN. First, we applied a novel convolution network in the backbone of Faster R-CNN, namely deformable convolution network (DCN), which improves the detection of lesions with varying shapes and sizes. Second, the original Faster R-CNN uses the output of the last layer of the backbone as a single-scale feature map. To facilitate the detection of small lesions, we used a multiscale feature pyramid network of multiple cross-scale connections between the different output layers of the backbone, called the neural architecture search-feature pyramid network (NAS-FPN). Thus, we were able to integrate the best features into the model. We then evaluated our method by using the datasets the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, respectively. Our method yielded a true positive rate of 0.9345 at 2.2805 false positive per image on CBIS-DDSM and a true positive rate of 0.9554 at 0.3829 false positive per image on INbreast.

Graphical abstract

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Akselrod-Ballin A, Karlinsky L, Hazan A, Bakalo R, Horesh AB, Shoshan Y, Barkan E (2017) Deep learning for automatic detection of abnormal findings in breast mammography. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, pp 321–329. doi:https://doi.org/10.1007/978-3-319-67558-9_37

  2. Al-antari MA, Al-masni MA, Choi M-T, Han S-M, Kim T-S (2018) A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. Int J Med Inform 117:44–54. https://doi.org/10.1016/j.ijmedinf.2018.06.003

    Article  PubMed  Google Scholar 

  3. Bunch PC, Hamilton JF, Sanderson GK, Simmons AH (1977) A free response approach to the measurement and characterization of radiographic observer performance. In: Application of Optical Instrumentation in Medicine VI. International Society for Optics and Photonics, pp 124–135. doi:https://doi.org/10.1117/12.955926

  4. Chen K, Wang J, Pang J, Cao Y, Xiong Y, Li X, Sun S, Feng W, Liu Z, Xu J (2019) MMDetection: open mmlab detection toolbox and benchmark. arXiv preprint arXiv:07155

  5. Dai J, Qi H, Xiong Y, Li Y, Zhang G, Hu H, Wei Y (2017) Deformable convolutional networks. In: Proceedings of the IEEE international conference on computer vision. pp 764–773

  6. Dhungel N, Carneiro G, Bradley AP (2015) Automated mass detection in mammograms using cascaded deep learning and random forests. In: 2015 international conference on digital image computing: techniques and applications (DICTA). IEEE, pp 1-8

  7. Ghiasi G, Lin T-Y, Le QV (2019) Nas-fpn: learning scalable feature pyramid architecture for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 7036–7045

  8. Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision. pp 1440–1448

  9. He K, Gkioxari G, Dollár P, Girshick R (2017) Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision. pp 2961–2969

  10. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 770–778

  11. Henriksen EL, Carlsen JF, Vejborg IM, Nielsen MB, Lauridsen CA (2019) The efficacy of using computer-aided detection (CAD) for detection of breast cancer in mammography screening: a systematic review. Acta Radiol 60:13–18. https://doi.org/10.1177/0284185118770917

    Article  PubMed  Google Scholar 

  12. Horsch A, Hapfelmeier A, Elter M (2011) Needs assessment for next generation computer-aided mammography reference image databases and evaluation studies. Int J Comput Assist Radiol Surg 6:749–767. https://doi.org/10.1007/s11548-011-0553-9

    Article  PubMed  Google Scholar 

  13. Jung H, Kim B, Lee I, Yoo M, Lee J, Ham S, Woo O, Kang J (2018) Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network. PloS One 13. doi:https://doi.org/10.1371/journal.pone.0203355

  14. Kozegar E, Soryani M, Minaei B, Domingues I (2013) Assessment of a novel mass detection algorithm in mammograms. J Cancer Res Ther 9:592. https://doi.org/10.4103/0973-1482.126453

    Article  PubMed  Google Scholar 

  15. Lauby-Secretan B, Scoccianti C, Loomis D, Benbrahim-Tallaa L, Bouvard V, Bianchini F, Straif K (2015) Breast-cancer screening—viewpoint of the IARC Working Group. N Engl J Med 372:2353–2358. https://doi.org/10.1056/NEJMsr1504363

    Article  CAS  PubMed  Google Scholar 

  16. Lee RS, Gimenez F, Hoogi A, Miyake KK, Gorovoy M, Rubin DL (2017) A curated mammography data set for use in computer-aided detection and diagnosis research. Sci Data 4:170177. https://doi.org/10.1038/sdata.2017.177

    Article  PubMed  PubMed Central  Google Scholar 

  17. Lehman CD, Wellman RD, Buist DS, Kerlikowske K, Tosteson AN, Miglioretti DL (2015) Diagnostic accuracy of digital screening mammography with and without computer-aided detection. https://doi.org/10.1001/jamainternmed20155231175:1828-1837

  18. Lin T-Y, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 2117–2125

  19. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88. https://doi.org/10.1016/j.media.2017.07.005

    Article  PubMed  Google Scholar 

  20. Løberg M, Lousdal ML, Bretthauer M, Kalager M (2015) Benefits and harms of mammography screening. Breast Cancer Res 17:63. https://doi.org/10.1186/s13058-015-0525-z

    Article  PubMed  PubMed Central  Google Scholar 

  21. Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS (2012) Inbreast: toward a full-field digital mammographic database. Acad Radiol 19:236–248. https://doi.org/10.1016/j.acra.2011.09.014

    Article  PubMed  Google Scholar 

  22. Morris EA (2016) Mammography: BI-RADS® update and tomosynthesis. In: Diseases of the brain, head and neck, spine 2016–2019. Springer, Cham, pp 347–349. doi:https://doi.org/10.1007/978-3-319-30081-8_37

  23. Organization WH (2018) Breast cancer. https://www.who.int/cancer/prevention/diagnosis-screening/breast-cancer/en/. Accessed 2019.4.1

  24. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66

    Article  Google Scholar 

  25. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems. pp 91–99

  26. Ribli D, Horváth A, Unger Z, Pollner P, Csabai I (2018) Detecting and classifying lesions in mammograms with deep learning. Sci Rep 8:1–7. https://doi.org/10.1038/s41598-018-22437-z

    Article  CAS  Google Scholar 

  27. Rimmer AJBBMJ (2017) Radiologist shortage leaves patient care at risk, warns royal college. BMJ 359. doi:https://doi.org/10.1136/bmj.j4683

  28. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115:211–252. https://doi.org/10.1007/s11263-015-0816-y

    Article  Google Scholar 

  29. SA F (1992) Breast masses. Mammographic and sonographic evaluation. Radiol Clin N Am 30:67–92

    Google Scholar 

  30. Samuelson FW, Petrick N (2006) Comparing image detection algorithms using resampling. In: 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006. IEEE, pp 1312-1315

  31. Shrivastava A, Gupta A, Girshick R (2016) Training region-based object detectors with online hard example mining. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 761–769

  32. Sprague BL, Arao RF, Miglioretti DL, Henderson LM, Buist DS, Onega T, Rauscher GH, Lee JM, Tosteson AN, Kerlikowske K (2017) National performance benchmarks for modern diagnostic digital mammography: update from the Breast Cancer Surveillance Consortium. Radiology 283:59–69. https://doi.org/10.1148/radiol.2017161519

    Article  PubMed  PubMed Central  Google Scholar 

  33. Teuwen J, van de Leemput S, Gubern-Mérida A, Rodriguez-Ruiz A, Mann R, Bejnordi BE (2018) Soft tissue lesion detection in mammography using deep neural networks for object detection

  34. Xie S, Girshick R, Dollár P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1492–1500

  35. Yassin NI, Omran S, El Houby EM, Allam H (2018) Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: a systematic review. Comput Methods Prog Biomed 156:25–45. https://doi.org/10.1016/j.cmpb.2017.12.012

    Article  Google Scholar 

  36. Zoph B, Le QV (2016) Neural architecture search with reinforcement learning. arXiv preprint arXiv:01578

Download references

Funding

This work was supported in part by KJYY20170724100440556 from Shenzhen Technical Project and JCYJ20160422113119640 from Shenzhen Municipal Science and Technology Innovation Project.

Author information

Authors and Affiliations

  1. School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060, Guangdong, People’s Republic of China

    Junchuan Peng, Changyu Bao & Weixiang Liu

  2. Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, 518060, Guangdong, People’s Republic of China

    Junchuan Peng, Changyu Bao & Weixiang Liu

  3. National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen University, Shenzhen, 518060, Guangdong, People’s Republic of China

    Junchuan Peng, Changyu Bao & Weixiang Liu

  4. Department of Breast and Thyroid Surgery, The Second People’s Hospital of Shenzhen, Shenzhen, 518035, Guangdong, China

    Chuting Hu, Xianming Wang & Wenjing Jian

Authors
  1. Junchuan Peng
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Changyu Bao
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Chuting Hu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Xianming Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Wenjing Jian
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Weixiang Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding authors

Correspondence to Xianming Wang or Weixiang Liu.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Peng, J., Bao, C., Hu, C. et al. Automated mammographic mass detection using deformable convolution and multiscale features. Med Biol Eng Comput 58, 1405–1417 (2020). https://doi.org/10.1007/s11517-020-02170-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-020-02170-4

Keywords