乳腺X线图像计算机辅助诊断技术综述

doi:10.3778/j.issn.1002-8331.2108-0205

摘要/Abstract

摘要： 近年来，乳腺癌严重威胁全球女性的身体健康，乳腺X线摄影是乳腺癌筛查的有效影像检查手段。乳腺X线图像计算机辅助诊断（computer aided diagnosis，CAD）运用计算机视觉、图像处理、机器学习等人工智能先进技术，自动分析处理乳腺X线图像，可为医生在临床中提供重要的诊断参考。主要面向肿块和微钙化病变检测、分割和分类等问题，从传统方法和深度学习方法两个角度，综述乳腺X线图像计算机辅助诊断技术的发展现状。鉴于近年来深度学习方法取得的突破性成果，回顾了经典的深度学习网络模型，着重介绍了深度学习方法在乳腺X线图像分析中的最新应用，对比分析了传统方法的弊端和深度学习方法的优势。对现有技术存在的问题进行分析，并对未来发展方向进行展望。

关键词: 乳腺X线图像, 计算机辅助诊断（CAD）, 计算机视觉, 深度学习, 人工智能

Abstract: In recent years, breast cancer has seriously threatened the health of women all over the world. Mammography is an effective imaging examination tool for breast cancer screening. Computer aided diagnosis（CAD） uses advanced artificial intelligence technologies such as computer vision, image processing, machine learning, etc. to automatically analyze and process mammographic images, which can provide important diagnostic references for doctors in clinical practice. This paper mainly focuses on the detection, segmentation and classification of masses and microcalcifications in mammograms, and reviews the development status of computer aided diagnosis technology in mammography, from the perspectives of traditional and deep learning methods. Considering the breakthrough achievements of deep learning, this paper reviews classical deep learning network models, focuses on the latest application of deep learning in mammography, and compares and analyzes the disadvantages of traditional methods and the advantages of deep learning methods. Finally, the problems of the existing technology are analyzed and the future development direction is prospected.

Key words: mammography, computer aided diagnosis（CAD）, computer vision, deep learning, artificial intelligence

陈智丽, 高皓, 潘以轩, 邢风. 乳腺X线图像计算机辅助诊断技术综述[J]. 计算机工程与应用, 2022, 58(4): 1-21.

CHEN Zhili, GAO Hao, PAN Yixuan, XING Feng. Review of Computer Aided Diagnosis Technology in Mammography[J]. Computer Engineering and Applications, 2022, 58(4): 1-21.

参考文献

[1] FERLAY J，COLOMBET M，SOERJOMATARAM I，et al.Estimating the global cancer incidence and mortality in 2018：GLOBOCAN sources and methods[J].International Journal of Cancer，2019，144（8）：1941-1953.
[2] 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（电子版），2020，12（3）：6-11.
ZHOU X T，SHEN S J，SUN Q.The status and progress of breast cancer screening in China[J].Frontier Journal of Chinese Medicine（Electronic Edition），2020，12（3）：6-11.
[3] WANG Y，LI J，GAO X.Latent feature mining of spatial and marginal characteristics for mammographic mass classification[J].Neurocomputing，2014，144：107118.
[4] 中国抗癌协会乳腺癌诊治指南与规范（2019年版）[J].中国癌症杂志，2019，29（8）：609-680.
Chinese Anti-Cancer Association guidelines and standards for the diagnosis and treatment of breast cancer（2019 edition）[J].Chinese Journal of Cancer，2019，29（8）：609-680.
[5] GUPTA R，UNDRILL P E.The use of texture analysis to delineate suspicious masses in mammography[J].Physics in Medicine and Biology，1995，40（5）：835.
[6] 郑光远，刘峡壁，韩光辉.医学影像计算机辅助检测与诊断系统综述[J].软件学报，2018，29（5）：1471-1514.
ZHENG G Y，LIU X B，HAN G H.A review of computer-aided detection and diagnosis systems for medical imaging[J].Journal of Software，2018，29（5）：1471-1514.
[7] HE W，JUETTE A，DENTON E R E，et al.A review on automatic mammographic density and parenchymal segmentation[J].International Journal of Breast Cancer，2015.doi：10.1155/2015/276217.
[8] BOWYER K，KOPANS D，KEGELMEYER W.The digital database for screening mammography[C]//3th International Workshop on Digital Mammography，1996，58：27.
[9] SUCKLING J，PARKER J，DANCE D.Mammographic image analysis society（MIAS） database v1.21[EB/OL].（2015）[2021-06-05].https：//www.repository.cam.ac.uk/handle/1810/250394.
[10] MATHEUS B R N，SCHIABEL H.Online mammographic images database for development and comparison of CAD schemes[J].Journal of Digital Imaging，2011，24（3）：500-506.
[11] MOREIRA I C，AMARAL I，DOMINGUES I.INbreast：toward a full-field digital mammographic database[J].Academic Radiology，2012，19（2）：236-248.
[12] EOM W Y，NEVE W D，RO Y M.Sparse feature analysis for detection of clustered microcalcifications in mammogram images[C]//International Forum on Medical Imaging in Asia 2012，the Japanese Society of Medical Imaging Technology，2012.
[13] LOPEZ M，POSADA N，MOURA D C.BCDR：a breast cancer digital repository[C]//15th International Conference on Experimental Mechanics，2012.
[14] LEE R S，GIMENEZ F，HOOGI A，et al.A curated mammography data set for use in computer-aided detection and diagnosis research[J].Scientific Data，2017，4（1）：1-9.
[15] EGAN J P，GREENBERG G Z，SCHULMAN A I.Operating characteristics，signal detectability，and the method of free response[J].The Journal of the Acoustical Society of America，1961，33（8）：993-1007.
[16] AKSELROD-BALLIN A，KARLINSKY L，ALPERT S，et al.A region based convolutional network for tumor detection and classification in breast mammography[C]//International Workshop on Deep Learning and Data Labeling for Medical Applications，2016：197-205.
[17] MOAYEDI F，AZIMIFAR Z，BOOSTANI R，et al.Contourlet-based mammography mass classification using the SVM family[J].Computers in Biology and Medicine，2010，40（4）：373-383.
[18] VIKHE P S，THOOL V R.Mass detection in mammographic images using wavelet processing and adaptive threshold technique[J].Journal of Medical Systems，2016，40（4）：82.
[19] 罗海驰，李岳阳，孙俊.一种基于自适应神经模糊推理系统的图像滤波方法[J].计算机科学，2013，40（7）：302-306.
LUO H C，LI Y Y，SUN J.An image filtering method based on adaptive neuro-fuzzy inference system[J].Computer Science，2013，40（7）：302-306.
[20] HAWKES P.Digital image processing[J].Nature，1978，276（5689）：740.
[21] PERONA P，MALIK J.Scale-space and edge detection using anisotropic diffusion[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，1990，12（7）：629-639.
[22] HAMISSI S，MEROUANI H F.Novel fully automated computer aided-detection of suspicious regions within mammograms[C]//2nd International Conference on the Innovative Computing Technology，2012：153-157.
[23] RIZZI M，D’ALOIA M，CASTAGNOLO B.Computer aided detection of microcalcifications in digital mammograms adopting a wavelet decomposition[J].Integrated Computer-Aided Engineering，2009，16（2）：91-103.
[24] PAPADOPOULOS A，FOTIADIS D I，COSTARIDOU L.Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques[J].Computers in Biology and Medicine，2008，38（10）：1045-1055.
[25] PIZER S M，AMBURN E P，AUSTIN J D，et al.Adaptive histogram equalization and its variations[J].Computer Vision，Graphics，and Image Processing，1987，39（3）：355-368.
[26] RADOVIC M，MILOSEVIC M，NINKOVIC S，et al.Para-
meter optimization of a computer-aided diagnosis system for detection of masses on digitized mammograms[J].Technology and Health Care，2015，23（6）：757-774.
[27] FAHNESTOCK J D，SCHOWENGERDT R A.Spatially variant contrast enhancement using local range modification[J].Optical Engineering，1983，22（3）：223378.
[28] STRICKLAND R N，HAHN H I.Wavelet transforms for detecting microcalcifications in mammograms[J].IEEE Transactions on Medical Imaging，1996，15（2）：218-229.
[29] MALLAT S，ZHONG S.Characterization of signals from multiscale edges[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，1992，14（7）：710-732.
[30] JAIN A，SINGH S，BHATEJA V.A robust approach for denoising and enhancement of mammographic images contaminated with high density impulse noise[J].International Journal of Convergence Computing，2013，1（1）：38-49.
[31] SHARMA S，KHANNA P.Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM[J].Journal of Digital Imaging，2015，28（1）：77-90.
[32] MUDIGONDA N R，RANGAYYAN R M，LEO DESAUTELS J E.Detection of breast masses in mammograms by density slicing and texture flow-field analysis[J].IEEE Transactions on Medical Imaging，2001，20（12）：1215-1227.
[33] CELIA V，TAHOCES P G，MéNDEZ A J，et al.Computerized detection of breast masses in digitized mammograms[J].Computers in Biology and Medicine，2007，37（2）：214-226.
[34] ELTONSY N H，TOURASSI G D，ELMAGHRABY A S.A concentric morphology model for the detection of masses in mammography[J].IEEE Transactions on Medical Imaging，2007，26（6）：880-889.
[35] GAO X，WANG Y，LI X，et al.On combining morpho-logical component analysis and concentric morphology model for mammographic mass detection[J].IEEE Transactions on Information Technology in Biomedicine，2009，14（2）：266-273.
[36] FISCHER U，BAUM F，OBENAUER S，et al.Comparative study in patients with microcalcifications：full-field digital mammography vs screen-film mammography[J].European Radiology，2002，12（11）：2679-2683.
[37] OLIVER A，TORRENT A，LLADó X，et al.Automatic microcalcification and cluster detection for digital and digitised mammograms[J].Knowledge-Based Systems，2012，28：68-75.
[38] YU S N，HUANG Y K.Detection of microcalcifications in digital mammograms using combined model-based and statistical textural features[J].Expert Systems with Applications，2010，37（7）：5461-5469.
[39] MALAR E，KANDASWAMY A，CHAKRAVARTHY D，et al.A novel approach for detection and classification of mammographic microcalcifications using wavelet analysis and extreme learning machine[J].Computers in Biology and Medicine，2012，42（9）：898-905.
[40] CHENG H D，LUI Y M，FREIMANIS R I.A novel approach to microcalcification detection using fuzzy logic technique[J].IEEE Transactions on Medical Imaging，1998，17（3）：442-450.
[41] 邵昊阳，张英涛，鲜敏，等.基于多域先验的乳腺超声图像协同分割[J].自动化学报，2016，42（4）：580-592.
SHAO H Y，ZHANG Y T，XIAN M，et al.Collaborative segmentation of breast ultrasound images based on multi-domain priors[J].Journal of Automatica Sinica，2016，42（4）：580-592.
[42] WANG X W，LI L H，LIU W，et al.An interactive system for computer-aided diagnosis of breast masses[J].Journal of Digital Imaging，2012，25（5）：570-579.
[43] DRUKKER K，GIGER M L，HORSCH K，et al.Computerized lesion detection on breast ultrasound[J].Medical Physics，2002，29（7）：1438-1446.
[44] CHEN D R，CHANG R F，KUO W J，et al.Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks[J].Ultrasound in Medicine & Biology，2002，28（10）：1301-1310.
[45] MEENALOSINI S，JANET J，KANNAN E.A novel approach in malignancy detection of computer aided diagnosis[J].American Journal of Applied Sciences，2012，9（7）：1020.
[46] 刘博，黄剑华，唐降龙，等.结合全局概率密度差异与局部灰度拟合的超声图像分割[J].自动化学报，2010，36（7）：951-959.
LIU B，HUANG J H，TANG J L，et al.Combining global probability density difference and local gray level fitting for ultrasonic image segmentation[J].Acta Automatica Sinica，2010，36（7）：951-959.
[47] KUPINSKI M A，GIGER M L.Automated seeded lesion segmentation on digital mammograms[J].IEEE Transactions on Medical Imaging，1998，17（4）：510-517.
[48] XU W D，XIA S R，XIAO M，et al.A model-based algorithm for mass segmentation in mammograms[C]//2005 IEEE 27th Annual Conference on Engineering in Medicine and Biology，2005：2543-2546.
[49] YU S S，TSAI C Y，LIU C C.A breast region extraction scheme for digital mammograms using gradient vector flow Snake[C]//4th International Conference on New Trends in Information Science and Service Science，2010：715-720.
[50] KARSSEMEIJER N，TE BRAKE G M.Detection of stellate distortions in mammograms[J].IEEE Transactions on Medical Imaging，1996，15（5）：611-619.
[51] TUNALI ?，KILI? E.Mass segmentation on mammograms using active contours[C]//21st Signal Processing and Communications Applications Conference，2013：1-4.
[52] DUBEY R，HANMANDLU M，GUPTA S.A comparison of two methods for the segmentation of masses in the digital mammograms[J].Computerized Medical Imaging and Graphics，2010，34（3）：185-191.
[53] CHU J，MIN H，LIU L，et al.A novel computer aided breast mass detection scheme based on morphological enhancement and SLIC superpixel segmentation[J].Medical Physics，2015，42（7）：3859-3869.
[54] JEN C C，YU S S.Automatic detection of abnormal mammograms in mammographic images[J].Expert Systems with Applications，2015，42（6）：3048-3855.
[55] DAVIES D，DANCE D.Automatic computer detection of clustered calcifications in digital mammograms[J].Physics in Medicine & Biology，1990，35（8）：1111-1118.
[56] RANGAYYAN R M，EL-FARAMAWY N M，DESAUTELS J L.Measures of acutance and shape for classification of breast tumors[J].IEEE Transactions on Medical Imaging，1997，16（6）：799-810.
[57] YANG S C，WANG C M，CHUNG Y N，et al.A computer-aided system for mass detection and classification in digitized mammograms[J].Biomedical Engineering：Applications，Basis and Communications，2005，17（5）：215-228.
[58] RASHED E A，ISMAIL I A，ZAKI S I.Multiresolution mammogram analysis in multilevel decomposition[J].Pattern Recognition Letters，2007，28（2）：286-292.
[59] NANNI L，BRAHNAM S，LUMINI A.A very high performing system to discriminate tissues in mammograms as benign and malignant[J].Expert Systems with Applications，2012，39（2）：1968-1971.
[60] VERMA B，ZAKOS J.A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques[J].IEEE Transactions on Information Technology in Biomedicine，2001，5（1）：46-54.
[61] ELTOUKHY M M，FAYE I，SAMIR B B.A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution representation[J].Computers in Biology and Medicine，2012，42（1）：123-128.
[62] MUDIGONDA N R，RANGAYYAN R，DESAUTELS J L.Gradient and texture analysis for the classification of mammographic masses[J].IEEE Transactions on Medical Imaging，2000，19（10）：1032-1043.
[63] JAGADEESH K，JAMUNALAKSMI K P，MUTHUVIDHYA P，et al.Mammogram based automatic computer aided detection of masses in medical images[J].Journal of Telecommunication Study，2018，3（1）：17-23.
[64] PUNITHA S，AMUTHAN A，JOSEPH K S.Benign and malignant breast cancer segmentation using optimized region growing technique[J].Future Computing and Informatics Journal，2018，3（2）：348-358.
[65] 万柏坤，王瑞平，朱欣，等.SVM算法及其在乳腺X片微钙化点自动检测中的应用[J].电子学报，2004（4）：587-590.
WAN B K，WANG R P，ZHU X，et al.SVM algorithm and its application in the automatic detection of microcalcification points on mammography[J].Acta Electronics，2004（4）：587-590.
[66] PAL N R，BHOWMICK B，PATEL S K，et al.A multi-stage neural network aided system for detection of microcalcifications in digitized mammograms[J].Neurocomputing，2008，71：2625-2634.
[67] KIM J K，PARK H W.Statistical textural features for detection of microcalcifications in digitized mammograms[J].IEEE Transactions on Medical Imaging，1999，18（3）：231-238.
[68] CHEN Z L，STRANGE H，OLIVER A，et al.Topological modeling and classification of mammographic microcalcification clusters[J].IEEE Transactions on Biomedical Engineering，2014，62（4）：1203-1214.
[69] HIROSE Y，YAMASHITA K，HIJIYA S.Back-propagation algorithm which varies the number of hidden units[J].Neural Networks，1991，4（1）：61-66.
[70] HINTON G E，SALAKHUTDINOV R R.Reducing the dimensionality of data with neural networks[J].Science，2006，313（5786）：504-507.
[71] BENGIO Y，LAMBLIN P，POPOVICI D.Greedy layer-wise training of deep networks[C]//Advances in Neural Information Processing Systems，2007：153-160.
[72] WAIBEL A，HANAZAWA T，HINTON G.Phoneme recognition using time-delay neural networks[J].IEEE Transactions on Acoustics，Speech，and Signal Processing，1989，37（3）：328-339.
[73] LECUN Y，KAVUKCUOGLU K，FARABET C.Convolutional networks and applications in vision[C]//2010 IEEE International Symposium on Circuits and Systems，2010：253-256.
[74] SAHINER B，CHAN H-P，PETRICK N，et al.Classification of mass and normal breast tissue：a convolution neural network classifier with spatial domain and texture images[J].IEEE Transactions on Medical Imaging，1996，15（5）：598-610.
[75] KRIZHEVSKY A，SUTSKEVER I，HINTON G E.Image-
net classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems，2012，25：1097-1105.
[76] 李华，杨嘉能，刘凤，等.基于深度学习的乳腺癌病理图像分类研究综述[J].计算机工程与应用，2020，56（13）：1-11.
LI H，YANG J N，LIU F，et al.Research review of breast cancer pathological image classification based on deep learning[J].Computer Engineering and Applications，2020，56（13）：1-11.
[77] VINCENT P，LAROCHELLE H，BENGIO Y，et al.Extracting and composing robust features with denoising autoencoders[C]//25th International Conference on Machine Learning，2008：1096-1103.
[78] HINTON G E，OSINDERO S，TEH Y W.A fast learning algorithm for deep belief nets[J].Neural Computation，2006，18（7）：1527-1554.
[79] LECUN Y，BENGIO Y.Convolutional networks for images，speech，and time series[J].The Handbook of Brain Theory and Neural Networks，1995，3361（10）：1-14.
[80] HE K，ZHANG X，REN S，et al.Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition，2016：770-778.
[81] IANDOLA F，MOSKEWICZ M，KARAYEV S，et al.Densenet：implementing efficient convnet descriptor pyramids[J].arXiv：1404.1869，2014.
[82] SMOLENSKY P.Information processing in dynamical systems：foundations of harmony theory[R].Colorado University at Boulder Dept of Computer Science，1986.
[83] NIE L，KUMAR A，ZHAN S.Periocular recognition using unsupervised convolutional RBM feature learning[C]//2014 22nd International Conference on Pattern Recognition，2014：399-404.
[84] NOROUZI M，RANJBAR M，MORI G.Stacks of convolutional restricted Boltzmann machines for shift-invariant feature learning[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition，2009：2735-2742.
[85] 康丽萍，许光銮，孙显.受限玻尔兹曼机的稀疏化特征学习[J].计算机科学，2016，43（12）：91-96.
KANG L P，XU G L，SUN X.Sparse feature learning of restricted Boltzmann machine[J].Computer Science，2016，43（12）：91-96.
[86] 李正权，林媛，李梦雅，等.基于判别式受限玻尔兹曼机的数字调制识别[J].通信学报，2021，42（2）：81-91.
LI Z Q，LIN Y，LI M Y，et al.Digital modulation recognition based on discriminant restricted Boltzmann machine[J].Journal on Communications，2021，42（2）：81-91.
[87] LITJENS G，KOOI T，BEJNORDI B E，et al.A survey on deep learning in medical image analysis[J].Medical Image Analysis，2017，42：60-88.
[88] LECUN Y，BOSER B，DENKER J S，et al.Back propagation applied to handwritten zip code recognition[J].Neural Computation，1989，1（4）：541-551.
[89] SIMONYAN K，ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].arXiv：1409.
1556，2014.
[90] DHUNGEL N，CARNEIRO G，BRADLEY A P.Automated mass detection in mammograms using cascaded deep learning and random forests[C]//2015 International Conference on Digital Image Computing：Techniques and Applications，2015：1-8.
[91] GIRSHICK R，DONAHUE J，DARRELL T，et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition，2014：580-587.
[92] REN S，HE K，GIRSHICK R，et al.Faster R-CNN：towards real-time object detection with region proposal networks[C]//Advances in Neural Information Processing Systems，2015，28：91-99.
[93] 吴帅，徐勇，赵东宁.基于深度卷积网络的目标检测综述[J].模式识别与人工智能，2018，31（4）：335-346.
WU S，XU Y，ZHAO D N.Overview of target detection based on deep convolutional networks[J].Pattern Recognition and Artificial Intelligence，2018，31（4）：335-346.
[94] AKSELROD-BALLIN A，KARLINSKY L，HAZAN A，et al.Deep learning for automatic detection of abnormal findings in breast mammography[M]//Deep learning in medical image analysis and multimodal learning for clinical decision support.Cham：Springer，2017：321-329.
[95] RIBLI D，HORVáTH A，UNGER Z，et al.Detecting and classifying lesions in mammograms with deep learning[J].Scientific Reports，2018，8（1）：1-7.
[96] JUNG H，KIM B，LEE I，et al.Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network[J].PloS One，2018，13（9）：e0203355.
[97] REDMON J，DIVVALA S，GIRSHICK R，et al.You only look once：unified，real-time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition，2016：779-788.
[98] AL-MASNI M A，AL-ANTARI M A，et al.Detection and classification of the breast abnormalities in digital mammograms via regional convolutional neural network[C]//2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society，2017：1230-1233.
[99] AL-MASNI M A，AL-ANTARI M A，PARK J M，et al.Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system[J].Computer Methods and Programs in Biomedicine，2018，157：85-94.
[100] REDMON J，FARHADI A.YOLOv3：an incremental improvement[J].arXiv：1804.02767，2018.
[101] DJEBBAR K，MIMI M，BERRADJA K.Deep convolutional neural networks for detection and classification of tumors in mammograms[C]//2019 6th International Conference on Image and Signal Processing and Their Applications，2019：1-7.
[102] CHOUKROUN Y，BAKALO R，BEN-ARI R，et al.Mammogram classification and abnormality detection from nonlocal labels using deep multiple instance neural network[C]//Eurographics Workshop on Visual Computing for Biology and Medicine，2017：11-19.
[103] HWANG S，KIM H E.Self-transfer learning for weakly supervised lesion localization[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention.Cham：Springer，2016：239-246.
[104] MORDANG J-J，JANSSEN T，BRIA A，et al.Automatic microcalcification detection in multi-vendor mammography using convolutional neural networks[C]//International Workshop on Breast Imaging.Cham：Springer，2016：35-42.
[105] WANG J，YANG Y.A context-sensitive deep learning approach for microcalcification detection in mammograms[J].Pattern Recognition，2018，78：12-22.
[106] SAMALA R K，CHAN H-P，HADJIISKI L M，et al.Deep-learning convolution neural network for computer-aided detection of microcalcifications in digital breast tomosynthesis[C]//Medical Imaging 2016：Computer-Aided Diagnosis.International Society for Optics and Photonics，2016，9785：97850Y.
[107] CAO Z，YANG Z，LIU X，et al.Deep learning based lesion detection for mammograms[C]//2019 IEEE International Conference on Healthcare Informatics，2019：1-3.
[108] LIN T Y，GOYAL P，GIRSHICK R，et al.Focal loss for dense object detection[C]//2017 IEEE International Conference on Computer Vision，2017：2980-2988.
[109] DHUNGEL N，CARNEIRO G，BRADLEY A P.Deep structured learning for mass segmentation from mammograms[C]//2015 IEEE International Conference on Image Processing，2015：2950-2954.
[110] DHUNGEL N，CARNEIRO G，BRADLEY A P.Deep learning and structured prediction for the segmentation of mass in mammograms[C]//International Conference on Medical Image Computing and Computer-assisted Intervention.Cham：Springer，2015：605-612.
[111] KALLENBERG M，PETERSEN K，NIELSEN M，et al.Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring[J].IEEE Transactions on Medical Imaging，2016，35（5）：1322-1331.
[112] PETERSEN K，CHERNOFF K，NIELSEN M，et al.NG.Breast density scoring with multiscale denoising autoencoders[C]//STMI Workshop at 15th International Conference on Medical Image Computing and Computer Assisted Intervention，2012.
[113] BYNG J W，BOYD N，FISHELL E，et al.The quantitative analysis of mammographic densities[J].Physics in Medicine & Biology，1994，39（10）：1629.
[114] YAN Y，CONZE P-H，QUELLEC G，et al.Two-stage breast mass detection and segmentation system towards automated high-resolution full mammogram analysis[J].arXiv：2002.12079，2020.
[115] LI H，CHEN D，NAILON W H，et al.Improved breast mass segmentation in mammograms with conditional residual U-Net[M]//Image analysis for moving organ，breast，and thoracic images.Cham：Springer，2018：81-89.
[116] SINGH V K，RASHWAN H A，ROMANI S，et al.Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network[J].Expert Systems with Applications，2020，139：112855.
[117] YAN Y，CONZE P H，DECENCIèRE E，et al.Cascaded multi-scale convolutional encoder-decoders for breast mass segmentation in high-resolution mammograms[C]//2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society，2019：6738-6741.
[118] RONNEBERGER O，FISCHER P，BROX T.U-net：convolutional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention.Cham：Springer，2015：234-241.
[119] MIN H，WILSON D，HUANG Y，et al.Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNN[C]//2020 IEEE 17th International Symposium on Biomedical Imaging，2020：1111-1115.
[120] CAO Z，YANG Z，ZHUO X，et al.DeepLIMA：deep learning based lesion identification in mammgrams[C]//2019 IEEE/CVF International Conference on Computer Vision Workshops，2019.
[121] LIN T-Y，DOLLáR P，GIRSHICK R，et al.Feature pyramid networks for object detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition，2017：2117-2125.
[122] WANG X，GIRSHICK R，GUPTA A，et al.Non-local neural networks[C]//2018 IEEE Conference on Computer Vision and Pattern Recognition，2018：7794-7803.
[123] WANG D，KHOSLA A，GARGEYA R.Deep learning for identifying metastatic breast cancer[J].arXiv：1606.
05718，2016.
[124] WU Y，HE K.Group normalization[C]//15th European Conference on Computer Vision，2018：3-19.
[125] NAIR V，HINTON G E.Rectified linear units improve restricted boltzmann machines[C]//International Conference of Machine Learning，2010.
[126] LEE D D，SEUNG H S.Learning the parts of objects by non-negative matrix factorization[J].Nature，1999，401（6755）：788-791.
[127] SONNENBURG S，R?TSCH G，SCH?FER C，et al.Large scale multiple kernel learning[J].The Journal of Machine Learning Research，2006，7：1531-1565.
[128] TRANSTRUM M K，SETHNA J P.Improvements to the Levenberg-Marquardt algorithm for nonlinear least-squares minimization[J].arXiv：1201.5885，2012.
[129] CARNEIRO G，NASCIMENTO J，BRADLEY A P.Unregistered multiview mammogram analysis with pre-trained deep learning models[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention.Cham：Springer，2015：652-660.
[130] AREVALO J，GONZáLEZ F A，RAMOS-POLLáN R，et al.Convolutional neural networks for mammography mass lesion classification[C]//2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society，2015：797-800.
[131] AREVALO J，GONZáLEZ F A，RAMOS-POLLáN R，et al.Representation learning for mammography mass lesion classification with convolutional neural networks[J].Computer Methods and Programs in Biomedicine，2016，127：248-257.
[132] FONSECA P，MENDOZA J，WAINER J，et al.Automatic breast density classification using a convolutional neural network architecture search procedure[C]//Medical Imaging 2015：Computer-Aided Diagnosis.International Society for Optics and Photonics，2015，9414：941428.
[133] PINTO N，DOUKHAN D，DICARLO J J，et al.A high-throughput screening approach to discovering good forms of biologically inspired visual representation[J].PLoS Computational Biology，2009，5（11）：e1000579.
[134] KOOI T，GUBERN-MERIDA A，MORDANG J J，et al.A comparison between a deep convolutional neural network and radiologists for classifying regions of interest in mammography[C]//International Workshop on Breast Imaging.Cham：Springer，2016：51-56.
[135] HUYNH B Q，LI H，GIGER M L.Digital mammographic tumor classification using transfer learning from deep convolutional neural networks[J].Journal of Medical Imaging，2016，3（3）：034501.
[136] JIAO Z，GAO X，WANG Y，et al.A deep feature based framework for breast masses classification[J].Neurocomputing，2016，197：221-231.
[137] LéVY D，JAIN A.Breast mass classification from mammograms using deep convolutional neural networks[J].arXiv：1612.00542，2016.
[138] WANG J，YANG X，CAI H，et al.Discrimination of breast cancer with microcalcifications on mammography by deep learning[J].Scientific Reports，2016，6（1）：1-9.
[139] BEKKER A J，SHALHON M，GREENSPAN H，et al.Multi-view probabilistic classification of breast microcalcifications[J].IEEE Transactions on Medical Imaging，2015，35（2）：645-653.
[140] FOTIN S V，YIN Y，HALDANKAR H，et al.Detection of soft tissue densities from digital breast tomosynthesis：comparison of conventional and deep learning approaches[C]//Medical Imaging 2016：Computer-Aided Diagnosis.International Society for Optics and Photonics，2016，9785：97850X.
[141] DUBROVINA A，KISILEV P，GINSBURG B，et al.Computational mammography using deep neural networks[J].Computer Methods in Biomechanics and Biomedical Engineering：Imaging & Visualization，2018，6（3）：243-247.
[142] KOOI T，LITJENS G，VAN GINNEKEN B，et al.Large scale deep learning for computer aided detection of mammographic lesions[J].Medical Image Analysis，2017，35：303-312.