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Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification

Part of the Lecture Notes in Computer Science book series (LNIP,volume 10435)

Abstract

Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods rely on regions of interest (ROIs) which require great efforts to annotate. Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning (MIL) for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned ROIs. We explore three different schemes to construct deep multi-instance networks for whole mammogram classification. Experimental results on the INbreast dataset demonstrate the robustness of proposed networks compared to previous work using segmentation and detection annotations. (Code: https://github.com/wentaozhu/deep-mil-for-whole-mammogram-classification.git).

Keywords

  • Deep multi-instance learning
  • Whole mammogram classification
  • Max pooling-based MIL
  • Label assignment-based MIL
  • Sparse MIL

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References

  1. American cancer society. what are the key statistics about breast cancer?

    Google Scholar 

  2. Ba, J., Kingma, D.: Adam: a method for stochastic optimization. In: ICLR (2015)

    Google Scholar 

  3. Ball, J.E., Bruce, L.M.: Digital mammographic computer aided diagnosis (cad) using adaptive level set segmentation. In: EMBS (2007)

    Google Scholar 

  4. Bowyer, K., Kopans, D., Kegelmeyer, W., et al.: The digital database for screening mammography. In: IWDM (1996)

    Google Scholar 

  5. Carneiro, G., Nascimento, J., Bradley, A.P.: Unregistered multiview mammogram analysis with pre-trained deep learning models. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 652–660. Springer, Cham (2015). doi:10.1007/978-3-319-24574-4_78

    CrossRef  Google Scholar 

  6. Dhungel, N., Carneiro, G., Bradley, A.P.: The automated learning of deep features for breast mass classification from mammograms. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 106–114. Springer, Cham (2016). doi:10.1007/978-3-319-46723-8_13

    CrossRef  Google Scholar 

  7. Dietterich, T.G., Lathrop, R.H., Lozano-Pérez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artif. Intell. 89(1), 31–71 (1997)

    CrossRef  MATH  Google Scholar 

  8. Domingues, I., Sales, E., Cardoso, J., Pereira, W.: Inbreast-database masses characterization. In: XXIII CBEB (2012)

    Google Scholar 

  9. Greenspan, H., van Ginneken, B., Summers, R.M.: Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE TMI 35(5), 1153–1159 (2016)

    Google Scholar 

  10. Hou, L., Samaras, D., Kurc, T.M., et al.: Patch-based convolutional neural network for whole slide tissue image classification arXiv:1504.07947 (2015)

  11. Jiao, Z., Gao, X., Wang, Y., Li, J.: A deep feature based framework for breast masses classification. Neurocomputing 197, 221–231 (2016)

    CrossRef  Google Scholar 

  12. Kooi, T., Litjens, G., van Ginneken, B., et al.: Large scale deep learning for computer aided detection of mammographic lesions. Med. Image Anal. 35, 303–312 (2017)

    CrossRef  Google Scholar 

  13. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1097–1105 (2012)

    Google Scholar 

  14. Moreira, I.C., Amaral, I., Domingues, I., et al.: Inbreast: toward a full-field digital mammographic database. Academic radiology (2012)

    Google Scholar 

  15. Oeffinger, K.C., Fontham, E.T., Etzioni, R., et al.: Breast cancer screening for women at average risk: 2015 guideline update from the American cancer society. Jama (2015)

    Google Scholar 

  16. Shen, W., Zhou, M., Yang, F., Dong, D., Yang, C., Zang, Y., Tian, J.: Learning from experts: developing transferable deep features for patient-level lung cancer prediction. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 124–131. Springer, Cham (2016). doi:10.1007/978-3-319-46723-8_15

    CrossRef  Google Scholar 

  17. Varela, C., Timp, S., Karssemeijer, N.: Use of border information in the classification of mammographic masses. Phys. Med. Biol. 51(2), 425 (2006)

    CrossRef  Google Scholar 

  18. Yan, Z., Zhan, Y., Peng, Z., et al.: Multi-instance deep learning: discover discriminative local anatomies for bodypart recognition. IEEE Trans. Med. Imaging 35(5), 1332–1343 (2016)

    CrossRef  Google Scholar 

  19. Zhu, W., Lan, C., Xing, J., et al.: Co-occurrence feature learning for skeleton based action recognition using regularized deep lstm networks. In: AAAI (2016)

    Google Scholar 

  20. Zhu, W., Miao, J., Qing, L., Huang, G.B.: Hierarchical extreme learning machine for unsupervised representation learning. In: IJCNN, pp. 1–8. IEEE (2015)

    Google Scholar 

  21. Zhu, W., Xie, X.: Adversarial deep structural networks for mammographic mass segmentation arXiv:1612.05970 (2016)

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Zhu, W., Lou, Q., Vang, Y.S., Xie, X. (2017). Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D., Duchesne, S. (eds) Medical Image Computing and Computer Assisted Intervention − MICCAI 2017. MICCAI 2017. Lecture Notes in Computer Science(), vol 10435. Springer, Cham. https://doi.org/10.1007/978-3-319-66179-7_69

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  • DOI: https://doi.org/10.1007/978-3-319-66179-7_69

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