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Multi-task Learning for Detection and Classification of Cancer in Screening Mammography

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12266)

Abstract

Breast screening is an effective method to identify breast cancer in asymptomatic women; however, not all exams are read by radiologists specialized in breast imaging, and missed cancers are a reality. Deep learning provides a valuable tool to support this critical decision point. Algorithmically, accurate assessment of breast mammography requires both detection of abnormal findings (object detection) and a correct decision whether to recall a patient for additional imaging (image classification). In this paper, we present a multi-task learning approach, that we argue is ideally suited to this problem. We train a network for both object detection and image classification, based on state-of-the-art models, and demonstrate significant improvement in the recall vs no recall decision on a multi-site, multi-vendor data set, measured by concordance with biopsy proven malignancy. We also observe improved detection of microcalcifications, and detection of cancer cases that were missed by radiologists, demonstrating that this approach could provide meaningful support for radiologists in breast screening (especially non-specialists). Moreover, we argue that this multi-task framework is broadly applicable to a wide range of medical imaging problems that require a patient-level recommendation, based on specific imaging findings.

Keywords

  • Decision support
  • Deep learning
  • RetinaNet
  • ResNet

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References

  1. Akselrod-Ballin, A., Karlinsky, L., Alpert, S., Hasoul, S., Ben-Ari, R., Barkan, E.: A region based convolutional network for tumor detection and classification in breast mammography. In: Carneiro, G., et al. (eds.) LABELS/DLMIA -2016. LNCS, vol. 10008, pp. 197–205. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46976-8_21

    CrossRef  Google Scholar 

  2. Akselrod-Ballin, A., et al.: Deep learning for automatic detection of abnormal findings in breast mammography. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 321–329. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_37

    CrossRef  Google Scholar 

  3. Fu, C.Y., Shvets, M., Berg, A.C.: RetinaMask: learning to predict masks improves state-of-the-art single-shot detection for free. arXiv preprint arXiv:1901.03353 (2019)

  4. Gao, F., Yoon, H., Wu, T., Chu, X.: A feature transfer enabled multi-task deep learning model on medical imaging. Expert Syst. Appl. 143, 112957 (2020)

    CrossRef  Google Scholar 

  5. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: International Conference on Computer Vision (2017)

    Google Scholar 

  6. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)

    Google Scholar 

  7. Jung, H., et al.: Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network. PLoS ONE 13(9), e0203355 (2018)

    CrossRef  Google Scholar 

  8. Kim, H.E., et al.: Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study. The Lancet Digit. Health 2(3), e138–e148 (2020)

    CrossRef  Google Scholar 

  9. Le, T.L.T., Thome, N., Bernard, S., Bismuth, V., Patoureaux, F.: Multitask classification and segmentation for cancer diagnosis in mammography. In: International Conference on Medical Imaging with Deep Learning - Extended Abstract Track, London, UK (2019)

    Google Scholar 

  10. Lehman, C.D., et al.: National performance benchmarks for modern screening digital mammography: update from the breast cancer surveillance consortium. Radiology 283(1), 49–58 (2017)

    CrossRef  Google Scholar 

  11. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)

    Google Scholar 

  12. Lotter, W., Sorensen, G., Cox, D.: A multi-scale CNN and curriculum learning strategy for mammogram classification. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 169–177. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_20

    CrossRef  Google Scholar 

  13. Mainiero, M.B., Parikh, J.R.: Recognizing and overcoming burnout in breast imaging. J. Breast Imaging 1(1), 60–63 (2019)

    CrossRef  Google Scholar 

  14. McKinney, S.M., et al.: International evaluation of an AI system for breast cancer screening. Nature 577(7788), 89–94 (2020)

    CrossRef  Google Scholar 

  15. Ribli, D., Horváth, A., Unger, Z., Pollner, P., Csabai, I.: Detecting and classifying lesions in mammograms with deep learning. Sci. Rep. 8(1), 1–7 (2018)

    CrossRef  Google Scholar 

  16. Seaman, S.R., White, I.R.: Review of inverse probability weighting for dealing with missing data. Stat. Methods Med. Res. 22(3), 278–295 (2013)

    MathSciNet  CrossRef  Google Scholar 

  17. Tabár, L., et al.: The incidence of fatal breast cancer measures the increased effectiveness of therapy in women participating in mammography screening. Cancer 125(4), 515–523 (2019)

    CrossRef  Google Scholar 

  18. Teare, P., Fishman, M., Benzaquen, O., Toledano, E., Elnekave, E.: Malignancy detection on mammography using dual deep convolutional neural networks and genetically discovered false color input enhancement. J. Digit. Imaging 30(4), 499–505 (2017). https://doi.org/10.1007/s10278-017-9993-2

    CrossRef  Google Scholar 

  19. International Agency for Research on Cancer: World Health Organization: Global cancer observatory database (2018)

    Google Scholar 

  20. Wu, N., et al.: Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans. Med. Imaging 39(4), 1184–1194 (2020)

    CrossRef  Google Scholar 

  21. Yala, A., Lehman, C., Schuster, T., Portnoi, T., Barzilay, R.: A deep learning mammography-based model for improved breast cancer risk prediction. Radiology 292(1), 60–66 (2019)

    CrossRef  Google Scholar 

  22. Yala, A., Schuster, T., Miles, R., Barzilay, R., Lehman, C.: A deep learning model to triage screening mammograms: a simulation study. Radiology 293(1), 38–46 (2019)

    CrossRef  Google Scholar 

  23. Zlocha, M., Dou, Q., Glocker, B.: Improving RetinaNet for CT lesion detection with dense masks from weak RECIST labels. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 402–410. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_45

    CrossRef  Google Scholar 

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Correspondence to David Richmond .

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Sainz de Cea, M.V., Diedrich, K., Bakalo, R., Ness, L., Richmond, D. (2020). Multi-task Learning for Detection and Classification of Cancer in Screening Mammography. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12266. Springer, Cham. https://doi.org/10.1007/978-3-030-59725-2_24

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  • DOI: https://doi.org/10.1007/978-3-030-59725-2_24

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