Abstract
Detecting tumor regions in breast ultrasound images has always been an interesting topic. Due to the complex structure of breasts and the existence of noise in the ultrasound images, traditional handcraft feature based methods usually cannot achieve satisfactory results. With the recent advance of deep learning, the performance of object detection has been boosted to a great extent, especially for general object detection. In this paper, we aim to systematically evaluate the performance of several existing state-of-the-art object detection methods for breast tumor detection. To achieve that, we have collected a new dataset consisting of 579 benign and 464 malignant lesion cases with the corresponding ultrasound images manually annotated by experienced clinicians. Comprehensive experimental results clearly show that the recently proposed convolutional neural network based method, Single Shot MultiBox Detector (SSD), outperforms other methods in terms of both precision and recall.
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References
Cheng, H.D., Shan, J., Ju, W., Guo, Y.H., Zhang, L.: Automated breast cancer detection and classification using ultrasound images: a survey. Pattern Recogn. 43, 299–317 (2010)
Su, Y., Wang, Y.: Automatic detection of the region of interest from breast tumor ultrasound image. Chin. J. Biomed. Eng. 29(2), 178–184 (2010)
Shan, J., Cheng, H.D., Wang, X.Y.: Completely automated segmentation approach for breast ultrasound images using multiple-domain features. Ultrasound Med. Biol. 38(2), 262–275 (2012)
Xian, M., Zhang, Y.T., Cheng, H.D.: Fully automatic segmentation of breast ultrasound images based on breast characteristics in space and frequency domains. Pattern Recogn. 48(2), 485–497 (2015)
Liu, B., Cheng, H.D., Huang, J.H., Tian, J.W., Tang, X.L., Liu, J.F.: Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images. Pattern Recogn. 43(1), 280–298 (2010)
Girshick, R.: Fast R-CNN. In: ICCV, pp. 1440–1448 (2015)
Ren, S.Q., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS (2015)
Redmon, J., Divvala, S.K., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: CVPR, pp. 779–788 (2015)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: single shot MultiBox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). doi:10.1007/978-3-319-46448-0_2
Akselrod-Ballin, A., Karlinsky, L., Alpert, S., Hasoul, S., Ben-Ari, R., Barkan, E.: A region based convolutional network for tumor detection and classification in breast mammography. In: Carneiro, G., et al. (eds.) LABELS/DLMIA -2016. LNCS, vol. 10008, pp. 197–205. Springer, Cham (2016). doi:10.1007/978-3-319-46976-8_21
Viola, P., Jones, M.: Robust real-time face detection. In: IJCV (2004)
Sande, K., Uijlings, J., Gevers, T., Smeulders, A.: Segmentation as selective search for object recognition. In: ICCV (2011)
Alexe, B., Deselaers, T., Ferrari, V.: What is an object? In: CVPR (2010)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)
Felzenszwalb, P., McAllester, D., Ramaman, D.: A discriminatively trained and multiscale: deformable part model. In: CVPR, pp. 1–8 (2008)
Ren, X.F., Ramanan, D.: Histograms of sparse codes for object detection. In: CVPR, pp. 3246–3253 (2013)
Ren, H.Y., Li, Z.N.: Object detection using generalization and efficiency balanced co-occurrence features. In: ICCV, pp. 46–54 (2015)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR, pp. 580–587 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 346–361. Springer, Cham (2014). doi:10.1007/978-3-319-10578-9_23
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2014)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). doi:10.1007/978-3-319-10590-1_53
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This work is supported by grants from the National Natural Science Foundation of China (61572109) and the Fundamental Research Funds for the Central Universities (ZYGX2016J164).
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Cao, Z. et al. (2017). Breast Tumor Detection in Ultrasound Images Using Deep Learning. In: Wu, G., Munsell, B., Zhan, Y., Bai, W., Sanroma, G., Coupé, P. (eds) Patch-Based Techniques in Medical Imaging. Patch-MI 2017. Lecture Notes in Computer Science(), vol 10530. Springer, Cham. https://doi.org/10.1007/978-3-319-67434-6_14
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DOI: https://doi.org/10.1007/978-3-319-67434-6_14
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