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[1]Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K. and Yuille, A. L., 2017. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE transactions on pattern analysis and machine intelligence, Vol. 40, no. 4, pp. 834-848. [2]Chougrad, H., Zouaki, H. and Alheyane, O., 2018. Deep convolutional neural networks for breast cancer screening. Computer methods and programs in biomedicine, Vol. 157, pp. 19-30. [3]Everingham, M., Van Gool, L., Williams, C. K., Winn, J. and Zisserman, A., 2010. The pascal visual object classes (VOC) challenge. International journal of computer vision, Vol. 88, pp. 303-338. [4]Girshick, R., 2015. Fast R-CNN. Proceedings of the IEEE international conference on computer vision, pp. 1440-1448. [5]Girshick, R., Donahue, J., Darrell, T. and Malik, J., 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 580-587. [6]Gu, Y., Xu, W., Lin, B., An, X., Tian, J., Ran, H., Ren, W., Chang, C., Yuan, J. and Kang, C., 2022. Deep learning based on ultrasound images assists breast lesion diagnosis in china: A multicenter diagnostic study. Insights into Imaging, Vol. 13, no. 1, Article 124. [7]He, K., Gkioxari, G., Dollár, P. and Girshick, R., 2017. Mask R-CNN. Proceedings of the IEEE international conference on computer vision, pp. 2961-2969. [8]He, K., Zhang, X., Ren, S. and Sun, J., 2016. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. [9]Hinton, G. E. and Salakhutdinov, R. R., 2006. Reducing the dimensionality of data with neural networks. science, Vol. 313, no. 5786, pp. 504-507. [10]Jabeen, K., Khan, M. A., Alhaisoni, M., Tariq, U., Zhang, Y.-D., Hamza, A., Mickus, A. and Damaševičius, R., 2022. Breast cancer classification from ultrasound images using probability-based optimal deep learning feature fusion. Sensors, Vol. 22, no. 3, Article 807. [11]Jaderberg, M., Simonyan, K., Zisserman, A. and Kavukcuoglu, K., 2015. Spatial transformer networks. Proceedings of the 28th International Conference on Neural Information Processing Systems, Montreal, Canada, Vol. 2, pp. 2017–2025. [12]Jia, M., Lin, X., Zhou, X., Yan, H., Chen, Y., Liu, P., Bao, L., Li, A., Basu, P. and Qiao, Y., 2020. Diagnostic performance of automated breast ultrasound and handheld ultrasound in women with dense breasts. Breast Cancer Research and Treatment, Vol. 181, pp. 589-597. [13]Kingma, D. P. and Ba, J., 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. [14]Krizhevsky, A., Sutskever, I. and Hinton, G. E., 2017. Imagenet classification with deep convolutional neural networks. Communications of the ACM, Vol. 60, no. 6, pp. 84-90. [15]LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P., 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, Vol. 86, no. 11, pp. 2278-2324. [16]Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y. and Berg, A. C., 2016. SSD: Single shot multibox detector. Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21-37. [17]Long, J., Shelhamer, E. and Darrell, T., 2015. Fully convolutional networks for semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431-3440. [18]Lu, W., Wang, Z., He, Y., Yu, H., Xiong, N. and Wei, J., 2019. Breast cancer detection based on merging four modes MRI using convolutional neural networks. ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1035-1039. [19]Neubeck, A. and Van Gool, L., 2006. Efficient non-maximum suppression. 18th international conference on pattern recognition (ICPR'06), Vol. 3, pp. 850-855. [20]Niu, L., Bao, L., Zhu, L., Tan, Y., Xu, X., Shan, Y., Liu, J., Zhu, Q., Jiang, C. and Shen, Y., 2019. Diagnostic performance of automated breast ultrasound in differentiating benign and malignant breast masses in asymptomatic women: A comparison study with handheld ultrasound. Journal of Ultrasound in Medicine, Vol. 38, no. 11, pp. 2871-2880. [21]Otsu, N., 1979. A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, Vol. 9, no. 1, pp. 62-66. [22]Raaj, R. S., 2023. Breast cancer detection and diagnosis using hybrid deep learning architecture. Biomedical Signal Processing and Control, Vol. 82, Article 104558. [23]Redmon, J. and Farhadi, A., 2018. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767. [24]Ren, S., He, K., Girshick, R. and Sun, J., 2015. Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28. [25]Ronneberger, O., Fischer, P. and Brox, T., 2015. U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp. 234-241. [26]Shoshan, Y., Bakalo, R., Gilboa-Solomon, F., Ratner, V., Barkan, E., Ozery-Flato, M., Amit, M., Khapun, D., Ambinder, E. B. and Oluyemi, E. T., 2022. Artificial intelligence for reducing workload in breast cancer screening with digital breast tomosynthesis. Radiology, Vol. 303, no. 1, pp. 69-77. [27]Simonyan, K. and Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. [28]Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. and Rabinovich, A., 2015. Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9. [29]Uijlings, J. R., Van De Sande, K. E., Gevers, T. and Smeulders, A. W., 2013. Selective search for object recognition. International journal of computer vision, Vol. 104, pp. 154-171. [30]Wang, C.-Y., Bochkovskiy, A. and Liao, H.-Y. M., 2023. Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464-7475. [31]Weyrich, N. and Warhola, G. T., 1998. Wavelet shrinkage and generalized cross validation for image denoising. IEEE Transactions on Image Processing, Vol. 7, no. 1, pp. 82-90. [32]Yuen, H., Princen, J., Illingworth, J. and Kittler, J., 1990. Comparative study of hough transform methods for circle finding. Image and vision computing, Vol. 8, no. 1, pp. 71-77. [33]Yurttakal, A. H., Erbay, H., İkizceli, T. and Karaçavuş, S., 2020. Detection of breast cancer via deep convolution neural networks using MRI images. Multimedia Tools and Applications, Vol. 79, pp. 15555-15573. [34]Zhang, Y., Chen, J.-H., Chang, K.-T., Park, V. Y., Kim, M. J., Chan, S., Chang, P., Chow, D., Luk, A. and Kwong, T., 2019. Automatic breast and fibroglandular tissue segmentation in breast MRI using deep learning by a fully-convolutional residual neural network U-Net. Academic radiology, Vol. 26, no. 11, pp. 1526-1535.
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