[1]徐立芳,傅智杰,莫宏伟.基于改进的YOLOv3算法的乳腺超声肿瘤识别[J].智能系统学报,2021,16(1):21-29.[doi:10.11992/tis.202010004]
 XU Lifang,FU Zhijie,MO Hongwei.Tumor recognition in breast ultrasound images based on an improved YOLOv3 algorithm[J].CAAI Transactions on Intelligent Systems,2021,16(1):21-29.[doi:10.11992/tis.202010004]
点击复制

基于改进的YOLOv3算法的乳腺超声肿瘤识别

参考文献/References:
[1] CHEN Wanqing, ZHENG Rongshou, BAADE P D, et al. Cancer statistics in China, 2015[J]. CA:a cancer journal for clinicians, 2016, 66(2):115-132.
[2] SIEGEL R L, MILLER K D, JEMAL A. Cancer statistics, 2016[J]. CA:a cancer journal for clinicians, 2016, 66(1):7-30.
[3] LO C S, WANG C M. Support vector machine for breast MR image classification[J]. Computers and mathematics with applications, 2012, 64(5):1153-1162.
[4] GINSBURG O, BRAY F, COLEMAN M P, et al. The global burden of women’s cancers:a grand challenge in global health[J]. The lancet, 2017, 389(10071):847-860.
[5] MENEZES G L, KNUTTEL F M, STEHOUWER B L, et al. Magnetic resonance imaging in breast cancer:a literature review and future perspectives[J]. World journal of clinical oncology, 2014, 5(2):61-70.
[6] 袁红梅, 余建群, 褚志刚, 等. 动态增强MRI、超声及X射线对乳腺良恶性病灶诊断的对比研究[J]. 中国普外基础与临床杂志, 2015, 22(2):246-250
YUAN Hongmei, YU Jianqun, CHU Zhigang, et al. Comparative study of dynamic contrast-enhanced breast MRI, ultrasound, and X-ray mammography in differential diagnosis of benign and malignant breast lesions[J]. Chinese journal of bases and clinics in general surgery, 2015, 22(2):246-250
[7] 中国抗癌协会乳腺癌专业委员会. 中国抗癌协会乳腺癌诊治指南与规范(2017年版)[J]. 中国癌症杂志, 2017,27(9):695-759
Breast cancer professional committee of Chinese anti-cancer association. Guidelines and specifications for breast cancer diagnosis and treatment of China anti cancer association (2017 Edition)[J]. China oncology, 2017,27(9):695-759
[8] 周星彤, 沈松杰, 孙强. 中国乳腺癌筛查现状及进展[J]. 中国医学前沿杂志, 2020, 12(3):6-11
ZHOU Xingtong, SHEN Songjie, SUN Qiang. Current situation and progress of breast cancer screening in China[J]. Chinese journal of the frontiers of medical science (electronic version), 2020, 12(3):6-11
[9] Cai L, Wang X, Wang Y, et al. Robust phase-based texture descriptor for classification of breast ultrasound images[J]. BioMedical Engineering OnLine, 2015, 14(1):1-21.
[10] HUANG Y L, JIANG Y R, CHEN D R, et al. Computer-aided diagnosis with morphological features for breast lesion on sonograms[J]. Ultrasound in obstetrics and gynecology, 2008, 32(4):565-572.
[11] KABIR S M, BHUIYAN M I H. Classification of breast tumour in contourlet transform domain[C]//2018 10th International Conference on Electrical and Computer Engineering (ICECE). Dhaka, Bangladesh, 2018:289-292.
[12] MENON R V, RAHA P, KOTHARI S, et al. Automated detection and classification of mass from breast ultrasound images[C]//2015 5th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics. Patna, India, 2015:1-4.
[13] 肖婷. 基于深度特征迁移与融合的乳腺超声图像分类[D]. 重庆:重庆大学, 2018.
XIAO Ting. Breast ultrasound image classification on deep feature based transfer learning and feature fusion[D]. Chongqing:Chongqing University, 2018.
[14] HAN S, KANG H K, JEONG J Y, et al. A deep learning framework for supporting the classification of breast lesions in ultrasound images[J]. Physics in medicine and biology, 2017, 62(19):7714-7728.
[15] 梁舒. 基于残差学习U型卷积神经网络的乳腺超声图像肿瘤分割研究[D]. 广州:华南理工大学, 2018.
LIANG Shu. Research on breast ultrasound image segmentaion based on residual U-shaped convolution neural network[D]. Guangzhou:South China University of Technology, 2018.
[16] 王恒立. 基于全卷积网络的乳腺超声图像语义分割方法[D]. 哈尔滨:哈尔滨工业大学, 2018.
WANG Hengli. Semantic segmentation method for breast ultrasound images based on fully convolutional network[D]. Harbin:Harbin Institute of Technology, 2018.
[17] YAP M H, GOYAL M, OSMAN F M, et al. End-to-end breast ultrasound lesions recognition with a deep learning approach[C]//Proceedings Volume 10578, Medical Imaging 2018:Biomedical Applications in Molecular, Structural, and Functional Imaging. Houston, Texas, United States, 2018:1057819.
[18] CHIAO J Y, CHEN K Y, LIAO K Y, et al. Detection and classification the breast tumors using mask R-CNN on sonograms[J]. Medicine, 2019, 98(19):e15200.
[19] SHIN S Y, LEE S, YUN I D, et al. Joint weakly and semi-supervised deep learning for localization and classification of masses in breast ultrasound images[J]. IEEE transactions on medical imaging, 2019, 38(3):762-774.
[20] REDMON J, FARHADI A. Yolov3:an incremental improvement[J]. arXiv preprint:arXiv:1804.02767, 2018.
[21] HU Jie, SHEN Li, SUN Gang. Squeeze-and-excitation networks[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018:7132-7141.
[22] GAO Shanghua, CHENG Mingming, ZHAO Kai, et al. Res2Net:a new multi-scale backbone architecture[J]. IEEE transactions on pattern analysis and machine intelligence, 2019:1-10.

备注/Memo

收稿日期:2020-10-09。
作者简介:徐立芳,讲师,博士,主要研究方向为智能控制、机器视觉与机器认知、人机混合智能。主持、参与省部级科研项目10项,授权发明专利6项。发表学术论文20余篇。;傅智杰,硕士研究生,主要研究方向为深度学习、计算机视觉、医学影像;莫宏伟,教授,博士生导师,主要研究方向为类脑计算与人工智能、机器视觉与机器认知、人机混合智能。主持省部级科研项目24项,授权发明专利10项。发表学术论文80余篇。
通讯作者:莫宏伟. E-mail:honwei2004@126.com

更新日期/Last Update: 2021-02-25
Copyright © 《 智能系统学报》 编辑部
地址:(150001)黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话:0451- 82534001、82518134 邮箱:tis@vip.sina.com