Abstract
Cervical cancer is increasingly threatening the health of women, then early screening and prevention of cervical cancer is very necessary. A traditional saliency cervical cancer detection method in Ultrasound image, assuming that there is only one salient object, is not conductive to practical application. Their effects are dependent on saliency threshold. Object detection model provides a kind of new solutions for multiple salient objects. Shot multiBox detector can accurately detect multi-objects with different scales simultaneously except for small cervical cancer regions. To overcome this drawback, this paper presents a new multi-saliency objects detection model, appending deconvolution module embedded within attention residual module. Experiments show that our proposed diagnosis algorithm achieves higher detection accuracy than comparison algorithms. Also, it improves detection performance for mult-saliency cervical cancer objects with small scales, which greatly improve the diagnosis accuracy of cervical cancer.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Lewis, G. C., Howson, et al. Cervical cancer detection.[J]. Medical Journal of Australia 1(13):708–710, 2017.
Koss, L. G., The Papanicolaou test for cervical cancer detection. A triumph and a tragedy[J]. International Journal of Gynecology & Obstetrics 30(1):79, 2016.
Reinhardt, M. J., Ehritt-Braun, C., Vogelgesang, D. et al., Metastatic Lymph Nodes in Patients with Cervical Cancer: Detection with MR Imaging and FDG PET1[J]. Radiology 218(3):776–782, 2001.
Zhang, J., Malmberg, F., and Sclaroff, S., Unconstrained Salient Object Detection.
Kaijian, X. et al., Optimization of diagnosis and treatment of chronic diseases based on association analysis under the background of regional integration. J. Med. Syst. 43:46, 2019.
Pengjiang, Q. et al., Knowledge-leveraged transfer fuzzy C -Means for texture image segmentation with self-adaptive cluster prototype matching. Knowl.-Based Syst. 130:33–50, 2017.
Zhang, J., et al. Salient Object Subitizing. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IEEE Computer Society, 2015.
Cheng, M. M., et al. Global Contrast Based Salient Region Detection. Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on IEEE, 2011.
Yan, Y. et al., Unsupervised Image Saliency Detection with Gestalt-laws Guided Optimization and Visual Attention Based Refinement. Pattern Recognition 32(12):23–41, 2018.
Li, G., and Yu, Y., Visual Saliency Detection Based on Multiscale Deep CNN Features. IEEE Transactions on Image Processing 25(11):5012–5024, 2016.
Klaes, R., Woerner, S. M., Ridder, R. et al., Detection of high-risk cervical intraepithelial neoplasia and cervical cancer by amplification of transcripts derived from integrated papillomavirus oncogenes.[J]. Cancer Research 59(24):6132–6136, 1999.
Benedet, J. L., Anderson, G. H., and Matisic, J. P., A comprehensive program for cervical cancer detection and management[J]. American Journal of Obstetrics and Gynecology 166(4):1254–1259, 2016.
Zhang, J., Liu, Y., Cervical Cancer Detection Using SVM Based Feature Screening[C]// Medical Image Computing & Computer-assisted Intervention-miccai, International Conference Saint-malo, France, September. DBLP, 2004.
Liu, F., and Gleicher, M., " [ACM Press the 18th annual ACM symposium - Seattle, WA, USA (2005.10.23–2005.10.26)] Proceedings of the 18th annual ACM symposium on User interface software and technology, − UIST \"05-Automatic image retracking with fisheye-view warping." (2005):153–175.
Valenti, R., Sebe, N., Gevers, T., Image saliency by isocentric curvedness and color[C]// IEEE Conference on Computer Vision. 2009.
Luo, Y. et al., Saliency Density Maximization for Efficient Visual Objects Discovery. IEEE Transactions on Circuits and Systems for Video Technology 21(12):1822–1834, 2011.
Feng, Jie, et al. Salient object detection by composition. 2015.
Yildirim, G. and Süsstrunk, S., FASA: Fast, Accurate, and Size-Aware Salient Object Detection. 2014.
Ren, S. et al., Object Detection Networks on Convolutional Feature Maps. IEEE Transactions on Pattern Analysis & Machine Intelligence 39(7):1476–1481, 2015.
Redmon, J., et al. You Only Look Once: Unified, Real-Time Object Detection. 2015.
Ren, S., et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. 2015.
Long, J., Shelhamer, E., and Darrell, T., Fully Convolutional Networks for Semantic Segmentation. IEEE Transactions on Pattern Analysis & Machine Intelligence 2014.
Zhou, Q. et al., Multi-scale deep context convolutional neural networks for semantic segmentation. World Wide Web 22(2):555–570, 2019.
He, K. et al., Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. IEEE Transactions on Pattern Analysis & Machine Intelligence 37(9):1904–1916, 2014.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
We declare that we have no conflict of interest.
Human and animal studies
The paper does not contain any studies with human participants or animals performed by any of the authors.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the Topical Collection on Image & Signal Processing
Rights and permissions
About this article
Cite this article
Wei, S., Dai, P. & Wang, Z. Cervical Cancer Detection and Diagnosis Based on Saliency Single Shot MultiBox Detector in Ultrasonic Elastography. J Med Syst 43, 250 (2019). https://doi.org/10.1007/s10916-019-1390-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10916-019-1390-6