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International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2019: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 pp 477–485Cite as

From Unilateral to Bilateral Learning: Detecting Mammogram Masses with Contrasted Bilateral Network

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

Abstract

The comparison of bilateral mammogram images is important for finding masses especially in dense breasts. However, most existing mammogram mass detection algorithms only considered unilateral image. In this paper, we propose a deep model called contrasted bilateral network (CBN) to take bilateral information into consideration. In CBN, Mask R-CNN is used as a basic framework, upon which two major modules are developed to exploit the bilateral information: distortion insensitive comparison module and logic guided bilateral module. The former one is designed to be robust to nonrigid distortion of bilateral registration, while the latter one integrates the bilateral domain knowledge of radiologist. Experimental results on DDSM dataset demonstrate that the proposed algorithm achieves the state-of-the-art performance.

Keywords

  • Mammogram mass
  • Object detection
  • Domain knowledge

Y. Liu and Z. Zhou—Equal contribution.

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References

  1. Dhungel, N., Carneiro, G., Bradley, A.P.: Automated mass detection in mammograms using cascaded deep learning and random forests. In: 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8. IEEE (2015)

    Google Scholar 

  2. Diniz, J.O.B., Diniz, P.H.B., Valente, T.L.A., Silva, A.C., de Paiva, A.C., Gattass, M.: Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks. Comput. Methods Programs Biomed. 156, 191–207 (2018)

    CrossRef  Google Scholar 

  3. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)

    Google Scholar 

  4. 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 

  5. Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, W.P.: The digital database for screening mammography. In: Proceedings of the 5th International Workshop on Digital Mammography, pp. 212–218. Medical Physics Publishing (2000)

    Google Scholar 

  6. 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 

  7. 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 

  8. Moreira, I.C., Amaral, I., Domingues, I., Cardoso, A., Cardoso, M.J., Cardoso, J.S.: INbreast: toward a full-field digital mammographic database. Acad. Radiol. 19(2), 236–248 (2012)

    CrossRef  Google Scholar 

  9. Mudigonda, N.R., Rangayyan, R.M., Desautels, J.L.: Detection of breast masses in mammograms by density slicing and texture flow-field analysis. IEEE Trans. Med. Imaging 20(12), 1215–1227 (2001)

    CrossRef  Google Scholar 

  10. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)

    Google Scholar 

  11. Ribli, D., Horváth, A., Unger, Z., Pollner, P., Csabai, I.: Detecting and classifying lesions in mammograms with deep learning. Sci. Rep. 8(1), 4165 (2018)

    CrossRef  Google Scholar 

  12. Siegel, R., Ma, J., Zou, Z., Jemal, A.: Cancer statistics, 2014. CA: A Cancer J. Clin. 64(1), 9–29 (2014)

    Google Scholar 

  13. Tai, S.C., Chen, Z.S., Tsai, W.T.: An automatic mass detection system in mammograms based on complex texture features. IEEE J. Biomed. Health Inform. 18(2), 618–627 (2014)

    CrossRef  Google Scholar 

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Acknowledgement

This work is funded by the National Natural Science Foundation of China (Grant No. 61625201, 61527804).

Author information

Authors and Affiliations

  1. Deepwise AI Lab, Beijing, China

    Yuhang Liu, Ling Luo, Qianyi Zhang, Xiuli Li, Yizhou Wang & Yizhou Yu

  2. Computer Science Department, Peking University, Beijing, China

    Zhen Zhou, Shu Zhang & Yizhou Wang

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Ling Luo

  4. Center for Data Science, Peking University, Beijing, China

    Fandong Zhang

  5. Peng Cheng Laboratory, Shenzhen, China

    Yizhou Wang

Authors
  1. Yuhang Liu
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  2. Zhen Zhou
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  3. Shu Zhang
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  4. Ling Luo
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  5. Qianyi Zhang
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  6. Fandong Zhang
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  7. Xiuli Li
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  8. Yizhou Wang
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  9. Yizhou Yu
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Corresponding author

Correspondence to Yizhou Yu .

Editor information

Editors and Affiliations

  1. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Dinggang Shen

  2. University of Georgia, Athens, GA, USA

    Prof. Tianming Liu

  3. Western University, London, ON, Canada

    Dr. Terry M. Peters

  4. Yale University, New Haven, CT, USA

    Lawrence H. Staib

  5. University of Strasbourg, Illkirch, France

    Caroline Essert

  6. United Imaging Intelligence, Shanghai, China

    Sean Zhou

  7. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Prof. Dr. Pew-Thian Yap

  8. Western University, London, ON, Canada

    Ali Khan

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Liu, Y. et al. (2019). From Unilateral to Bilateral Learning: Detecting Mammogram Masses with Contrasted Bilateral Network. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11769. Springer, Cham. https://doi.org/10.1007/978-3-030-32226-7_53

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  • DOI: https://doi.org/10.1007/978-3-030-32226-7_53

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