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.
Y. Liu and Z. Zhou—Equal contribution.
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
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Siegel, R., Ma, J., Zou, Z., Jemal, A.: Cancer statistics, 2014. CA: A Cancer J. Clin. 64(1), 9–29 (2014)
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)
Acknowledgement
This work is funded by the National Natural Science Foundation of China (Grant No. 61625201, 61527804).
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
Liu, Y. et al. (2019). From Unilateral to Bilateral Learning: Detecting Mammogram Masses with Contrasted Bilateral Network. In: Shen, D., 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
Download citation
DOI: https://doi.org/10.1007/978-3-030-32226-7_53
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32225-0
Online ISBN: 978-3-030-32226-7
eBook Packages: Computer ScienceComputer Science (R0)
