Abstract
Evaluation and diagnosis of breast cancer will be more and more vital in medical field. A general solution to breast cancer cellularity is to modify output of a state-of-the-art classification backbone to prediction a score between 0 and 1. However, this solution does not take clinical meaning of cancer cellularity which defined as proportion of cancer cells over image patches into consideration. In this paper, a segmentation guided regression network is proposed for breast cancer cellularity, adding more semantic detailed features for regression task. Consequently, the proposed method can not only take advantage of global context features from classification backbone, but also position feature and texture feature from segmentation network. A powerful segmentation network with 0.8438 mean Intersection-over-Union is obtained on extremely class imbalanced datasets. The proposed method with Resnet101 as regression backbone gets PK value of 0.9260 and L1 loss of 0.0719.
Y. Wang—Currently working toward the Master degree in the School of Electric Information and Communications, HuaZhong University of Science and Technology.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics. CA Cancer J. Clin. 65(1), 5–29 (2015)
Symmans, W.F., et al.: Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy. J. Clin. Oncol. 25(28), 4414–4422 (2007)
Thompson, A.M., Moulder-Thompson, S.L.: Neoadjuvant treatment of breast cancer. Ann. Oncol. 23, x231–x236 (2012)
Hermanek, P., Wittekind, C.: Residual tumor (R) classification and prognosis. In: Seminars in Surgical Oncology (1994)
Peikari, M., Salama, S., Nofech-Mozes, S., et al.: Automatic cellularity assessment from post-treated breast surgical specimens. Cytom. Part A 91(11), 1078–1087 (2017)
Wang, Z., Liu, C., Cheng, D.: Automated detection of clinically significant prostate cancer in mp-MRI images based on an end-to-end deep neural network. IEEE Trans. Med. Imaging 37, 1127–1139 (2018)
Mehta, S., Mercan, E., Bartlett, J.: Y-Net: joint segmentation and classification for diagnosis of breast biopsy images. CoRR, abs/1806.01313 (2018)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: The IEEE Conference on Computer Vision and Pattern Recognition (2015)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. CoRR abs/1505.04597 (2015)
Chen, L.-C., Zhu, Y.: Encoder-decoder with atrous separable convolution for semantic image segmentation. arXiv:1802.02611 (2018)
Chen, L.-C., Papandreou, G., Schroff, F.: Rethinking atrous convolution for semantic image segmentation. CoRR, abs/1706.0558 (2017)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. CoRR, abs/1502.03167 (2015)
Lin, T.-Y., et al.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)
Lin, T.-Y., Goyal, P.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. (2018)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794–7803 (2018)
He, K., Zhang, X.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Computer Vision and Pattern Recognition (2017)
Jie, H., Li, S.: Squeeze-and-excitation networks. arXiv preprint arXiv:1709.01507 (2017)
Zoph, B., Vasudevan, V., Shlens, J.: Learning transferable architectures for scalable image recognition. arXiv preprint arXiv:1707.07012 (2017)
Smith, W.D., Dutton, R.C., Smith, N.T.: A measure of assosication for assessing prediction accuracy that is a generalization of non-parameteric ROC area. Stat. Med. 15(11), 1199 (1996)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, Y., Yu, L., Wang, S. (2019). Segmentation Guided Regression Network for Breast Cancer Cellularity. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-31723-2_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31722-5
Online ISBN: 978-3-030-31723-2
eBook Packages: Computer ScienceComputer Science (R0)