Breast Mass Recognition Model via Deep-Level Pathological Information Mining

doi:10.3778/j.issn.1673-9418.2008028

Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (2): 413-427.DOI: 10.3778/j.issn.1673-9418.2008028

• Artificial Intelligence • Previous Articles Next Articles

Breast Mass Recognition Model via Deep-Level Pathological Information Mining

LI Guangli¹, YUAN Tian¹, LI Chuanxiu¹, WU Renzhong², ZHUO Jianwu¹, ZHANG Hongbin²^,⁺()

1. School of Information Engineering, East China Jiaotong University, Nanchang 330013, China
2. School of Software, East China Jiaotong University, Nanchang 330013, China

Received:2020-08-10 Revised:2020-10-21 Online:2022-02-01 Published:2020-11-06
About author:LI Guangli, born in 1977, M.S., associate professor, member of CCF. Her research interests include medical image analysis, cross-media retrieval and recommendation system.
YUAN Tian, born in 1994, M.S. candidate, student member of CCF. His research interests include tumor image recognition and machine learning.
LI Chuanxiu, born in 1995, M.S. candidate. His research interests include tumor image recognition and deep learning.
WU Renzhong, born in 1995, M.S. Her research interests include breast cancer recognition and machine learning.
ZHUO Jianwu, born in 1994, M.S. candidate. His research interests include natural language processing and deep learning.
ZHANG Hongbin, born in 1979, Ph.D., associate professor, senior member of CCF. His research interests include natural language processing, image recognition, recommendation system, etc.
Supported by:
National Natural Science Foundation of China(62161011);National Natural Science Foundation of China(61762038);National Natural Science Foundation of China(61861016);Humanities and Social Science Research Planning Fund of Ministry of Education(20YJAZH142);Key Research and Development Program of Jiangxi Provincial Department of Science and Technology(20192BBE50071);Key Research and Development Program of Jiangxi Provincial Department of Science and Technology(20202BBEL53003);Science and Technology Project of Jiangxi Provincial Department of Education(GJJ190323);Science and Technology Project of Jiangxi Provincial Department of Education(GJJ200644);Social Science Foundation of Jiangxi Higher Education(TQ19101);Social Science Foundation of Jiangxi Higher Education(TQ20108);Natural Science Foundation of Jiangxi Province(20202BABL202044);Natural Science Foundation of Jiangxi Province(20212BAB202006)

融入深层病理信息挖掘的乳腺肿块识别模型

李广丽¹, 袁天¹, 李传秀¹, 邬任重², 卓建武¹, 张红斌²^,⁺()

1.华东交通大学信息工程学院,南昌 330013
2.华东交通大学软件学院,南昌 330013

通讯作者: + E-mail: zhanghongbin@whu.edu.cn
作者简介:李广丽（1977—）,女,广西博白人,硕士,副教授,CCF会员,主要研究方向为医学图像分析、跨媒体检索、推荐系统。
袁天（1994—）,男,湖北孝感人,硕士研究生, CCF学生会员,主要研究方向为肿瘤图像识别、机器学习。
李传秀（1995—）,男,山东菏泽人,硕士研究生,主要研究方向为肿瘤图像识别、深度学习。
邬任重（1995—）,女,江西丰城人,硕士,主要研究方向为乳腺肿瘤识别、机器学习。
卓建武（1994—）,男,广东汕尾人,硕士研究生,主要研究方向为自然语言处理、深度学习。
张红斌（1979—）,男,江苏如皋人,博士,副教授,CCF高级会员,主要研究方向为自然语言处理、图像识别、推荐系统等。
基金资助:
国家自然科学基金(62161011);国家自然科学基金(61762038);国家自然科学基金(61861016);教育部人文社会科学研究规划基金(20YJAZH142);江西省科技厅重点研发计划(20192BBE50071);江西省科技厅重点研发计划(20202BBEL53003);江西省教育厅科技项目(GJJ190323);江西省教育厅科技项目(GJJ200644);江西省高校人文社科基金(TQ19101);江西省高校人文社科基金(TQ20108);江西省自然科学基金面上项目(20202BABL202044);江西省自然科学基金面上项目(20212BAB202006)

Abstract

Abstract:

Breast cancer is the most common variant of cancer in women. Breast mass recognition model can assist pathologists in formulating their clinical diagnoses more efficiently. However, sample scarcity in the field of medical image analysis usually causes the overfitting problem. A novel breast mass recognition model via deep-level pathological information mining is proposed in this paper to address this problem. Firstly, a new sample refinement strategy is constructed to obtain image samples with high-quality across different mammographic datasets, which mainly deals with the sample scarcity problem from the data augmentation perspective. The deep-level pathological information contained in the limited labeled samples is mined out in turn from the shallower to the deeper, which mainly copes with the sample scarcity problem from the feature selection perspective. A novel feature selection algorithm called multi-view efficient range-based gene selection (MvERGS) is proposed to improve the discriminant ability of each image feature and reduce the corresponding dimensions, which helps to fit sample size well. Then the state-of-the-art discriminant correlation analysis (DCA) method is employed to analyze the deep cross-modal correlations among diverse refined features, which is used to depict the lesion areas in mammographs more accurately. Finally, based on deep-level pathological information and traditional classifier, an effective breast mass classification model is trained. Extensive experimental results demonstrate that the proposed breast mass classification model is superior to most baselines in some key metrics, including Accuracy and AUC, and it can cope with the overfitting problem very well.

Key words: breast mass recognition, pathological information mining, sample refinement, feature selection, multi-view efficient range-based gene selection (MvERGS)

摘要：

乳腺癌是女性中最常见的癌症,乳腺肿块识别模型能有效地辅助医生的临床诊断工作。然而,医学图像样本稀缺使识别模型易过拟合。提出融入深层病理信息挖掘的乳腺肿块识别模型：构建样本精选策略,跨越不同乳腺造影图像数据集筛选优质样本,从数据增强角度应对医学图像样本稀缺;由浅入深挖掘有限标注样本中蕴含的病理信息,从特征优选角度应对医学图像样本稀缺。设计多视角有效区域基因优选（MvERGS）算法,以精化原始图像特征,提升特征判别性并压缩特征维度,更好地匹配样本数量;对精化的新特征执行判别相关分析（DCA）,深入挖掘异构特征间的跨模态相关性,即深层病理信息,以准确刻画乳腺肿块病灶区域。基于深层病理信息与传统分类器训练出高效的乳腺肿块识别模型,完成乳腺造影图像分类。实验表明：识别模型的关键技术指标,包括Accuracy和AUC,均优于主流基线,样本稀缺导致的过拟合问题得到缓解。

关键词: 乳腺肿块识别, 病理信息挖掘, 样本精选, 特征优选, 多视角有效区域基因优选（MvERGS）

CLC Number:

TP391

LI Guangli, YUAN Tian, LI Chuanxiu, WU Renzhong, ZHUO Jianwu, ZHANG Hongbin. Breast Mass Recognition Model via Deep-Level Pathological Information Mining[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(2): 413-427.

李广丽, 袁天, 李传秀, 邬任重, 卓建武, 张红斌. 融入深层病理信息挖掘的乳腺肿块识别模型[J]. 计算机科学与探索, 2022, 16(2): 413-427.

Figures/Tables 9

Fig.1 Framework of RMD model

Table 1 Detailed information about CBIS-DDSM and INbreast datasets

数据集	预处理后尺寸/像素	阴性样本数/幅	阳性样本数/幅	训练-测试比
CBIS-DDSM	1 152×896	1 434	1 347	7:3
INbreast	2 500×3 300	287	100	7:3

Table 2 Comparison of RMD and baselines on CBIS-DDSM dataset

设置	模型	Acc/%	AUC/%	设置	模型	Acc/%	AUC/%
训练-测试：85-15 图像：Whole	RESNET-RESNET^[29]	—	87.00	训练-测试：70-30 图像：Whole	GS-XGBoost^[37]	78.71	85.94
	RESNET-VGG^[29]	—	88.00		VGG16^[35]	50.46	51.21
	VGG-VGG^[29]	—	86.00		ResNet152^[34]	50.46	53.22
	VGG-RESNET^[29]	—	88.00		DenseNet121^[33]	50.69	54.69
	Model Averaging^[29]	—	91.00		Fisher Score^[39]	80.50	90.65
	GS-XGBoost^[37]	76.99	72.81		ERGS^[38]	79.31	93.73
	M-SVM	77.51	85.17		PSO^[15]	74.40	83.36
	M-LR	77.03	86.36		HGSCCA^[21]	53.23	50.00
	MD-XGBoost	77.75	77.98		M-RF	83.49	97.98
	MD-LR	77.27	85.16		M-SVM	81.34	98.60
	RMD-XGBoost	77.65	77.59		MD-GBDT	91.39	96.03
	RMD-NB	77.19	84.84		MD-NB	81.10	98.24
图像：ROI	Tsochatzidis^[24]	74.90	80.40		RMD-NB	82.66	93.56
图像：ROI	Rampun^[27]	80.40	84.00	RMD-RF		67.58	94.21

Table 3 Comparison of RMD and baselines on INbreast dataset

设置	模型	Acc/%	AUC/%	设置	模型	Acc/%	AUC/%
训练-测试：70-30 图像：Whole	RESNET-RESNET^[29]	—	95.00	训练-测试：70-30 图像：Whole	PSO^[15]	79.31	80.43
	RESNET-VGG^[29]	—	95.00		HGSCCA^[21]	78.56	50.00
	VGG-VGG^[29]	—	95.00		M-GBDT	85.34	83.08
	VGG-RESNET^[29]	—	95.00		M-LR	83.62	86.24
	Model Averaging^[29]	—	98.00		MD-LR	89.66	91.48
	GS-XGBoost^[37]	85.35	82.84		MD-GBDT	87.07	93.00
	VGG16^[35]	75.86	52.52		RMD-NB	77.59	81.94
	ResNet152^[34]	75.86	54.14		RMD-SVM	75.86	82.79
	DenseNet121^[33]	75.86	62.01	图像：ROI	CNN^[42]	84.00±4	69.00±10
	Fisher Score^[39]	81.03	88.51		SMIL^[40]	90.00±2	89.00±3
	ERGS^[38]	81.89	78.41		Carneiro^[41]	—	86.00

Table 4 Recognition performance of original image features

Dataset	Feature	Acc/%	Pre_Neg/%	Pre_Pos/%	Sen/%	Spe/%	AUC/%	TP	TN	FP	FN
CBIS-DDSM	S	81.34	73.91	97.70	62.96	98.61	95.24	255	425	6	150
	G	67.34	65.80	69.64	57.78	76.33	67.06	234	329	102	171
	H	63.64	63.60	63.69	58.02	68.91	68.05	235	297	134	170
	L	55.50	57.58	53.69	59.26	51.97	58.76	240	224	207	165
	D	52.27	53.21	50.89	42.47	61.48	51.98	172	265	166	233
	R	58.97	59.52	58.29	53.83	63.81	62.32	218	275	156	187
	V	54.31	54.97	53.35	45.19	62.88	55.70	183	271	160	222
INbreast	S	77.59	77.68	75.00	10.71	98.86	54.79	3	87	1	25
	G	75.86	75.86	0	0	100.00	58.81	0	88	0	28
	H	75.86	76.32	50.00	3.57	98.86	48.25	1	87	1	27
	L	75.86	75.86	0	0	100.00	50.00	0	88	0	28
	D	78.45	78.38	80.00	14.29	98.86	61.63	4	87	1	24
	R	75.86	75.86	0	0	100.00	53.08	0	88	0	28
	V	78.45	77.88	100.00	10.71	100.00	55.36	3	88	0	25

Table 5 Recognition performance based on MvERGS algorithm

Dataset	Feature	Acc/%	Pre_Neg/%	Pre_Pos/%	Sen/%	Spe/%	AUC/%	TP	TN	FP	FN
CBIS- DDSM	$S ˜$	82.89↑	75.44↑	98.52↑	65.68↑	99.07↑	97.71↑	266↑	427↑	4↑	139↑
	$G ˜$	64.59	64.15	65.18	57.78	71.00	70.78↑	234	306	125	171
	$H ˜$	63.76↑	63.62↑	63.93↑	57.78	69.37↑	63.58	234	299↑	132↑	171
	$L ˜$	66.03↑	67.13↑	64.86↑	65.19↑	66.82↑	72.43↑	264↑	288↑	143↑	141↑
	$D ˜$	55.38↑	55.56↑	55.10↑	42.72↑	67.29↑	55.00↑	173↑	290↑	141↑	232↑
	$R ˜$	61.00↑	63.29↑	58.96↑	64.20↑	58.00	62.86↑	260↑	250	181	145↑
	$V ˜$	57.30↑	57.09↑	57.64↑	44.69	69.14↑	57.88↑	181	298↑	133↑	224
	Avg₁	61.91	61.23	63.89	54.22	69.14	65.59	220	298	133	185
	Avg₂	64.42↑	63.75↑	66.31↑	56.86↑	71.53↑	68.60↑	230↑	308↑	123↑	175↑
INbreast	$S ˜$	81.03↑	83.00↑	68.75	39.29↑	94.32	61.16↑	11↑	83	5	17↑
	$G ˜$	76.72↑	81.44↑	52.63↑	35.71↑	89.77	65.50↑	10↑	79	9	18↑
	$H ˜$	76.72↑	76.99↑	66.67↑	7.14↑	98.86	53.00↑	2↑	87	1	26↑
	$L ˜$	77.59↑	78.70↑	62.50↑	17.86↑	96.59	57.22↑	5↑	85	3	23↑
	$D ˜$	76.72	77.98	57.14	14.29↑	96.59	55.44	4	85	3	24
	$R ˜$	80.17↑	80.95↑	72.73↑	28.57↑	96.59	75.57↑	8↑	85	3	20↑
	$V ˜$	77.59	78.18↑	66.67	14.29↑	97.73	64.98↑	4↑	86	2	24↑
	Avg₃	76.85	76.83	43.57	5.61	99.51	54.79	2	88	1	26
	Avg₄	78.08↑	79.61↑	63.87↑	22.45↑	95.78	61.84↑	6↑	84	4	22↑

Table 5 Recognition performance based on MvERGS algorithm

Dataset	Feature	Acc/%	Pre_Neg/%	Pre_Pos/%	Sen/%	Spe/%	AUC/%	TP	TN	FP	FN
CBIS- DDSM	$S ˜$	82.89↑	75.44↑	98.52↑	65.68↑	99.07↑	97.71↑	266↑	427↑	4↑	139↑
	$G ˜$	64.59	64.15	65.18	57.78	71.00	70.78↑	234	306	125	171
	$H ˜$	63.76↑	63.62↑	63.93↑	57.78	69.37↑	63.58	234	299↑	132↑	171
	$L ˜$	66.03↑	67.13↑	64.86↑	65.19↑	66.82↑	72.43↑	264↑	288↑	143↑	141↑
	$D ˜$	55.38↑	55.56↑	55.10↑	42.72↑	67.29↑	55.00↑	173↑	290↑	141↑	232↑
	$R ˜$	61.00↑	63.29↑	58.96↑	64.20↑	58.00	62.86↑	260↑	250	181	145↑
	$V ˜$	57.30↑	57.09↑	57.64↑	44.69	69.14↑	57.88↑	181	298↑	133↑	224
	Avg₁	61.91	61.23	63.89	54.22	69.14	65.59	220	298	133	185
	Avg₂	64.42↑	63.75↑	66.31↑	56.86↑	71.53↑	68.60↑	230↑	308↑	123↑	175↑
INbreast	$S ˜$	81.03↑	83.00↑	68.75	39.29↑	94.32	61.16↑	11↑	83	5	17↑
	$G ˜$	76.72↑	81.44↑	52.63↑	35.71↑	89.77	65.50↑	10↑	79	9	18↑
	$H ˜$	76.72↑	76.99↑	66.67↑	7.14↑	98.86	53.00↑	2↑	87	1	26↑
	$L ˜$	77.59↑	78.70↑	62.50↑	17.86↑	96.59	57.22↑	5↑	85	3	23↑
	$D ˜$	76.72	77.98	57.14	14.29↑	96.59	55.44	4	85	3	24
	$R ˜$	80.17↑	80.95↑	72.73↑	28.57↑	96.59	75.57↑	8↑	85	3	20↑
	$V ˜$	77.59	78.18↑	66.67	14.29↑	97.73	64.98↑	4↑	86	2	24↑
	Avg₃	76.85	76.83	43.57	5.61	99.51	54.79	2	88	1	26
	Avg₄	78.08↑	79.61↑	63.87↑	22.45↑	95.78	61.84↑	6↑	84	4	22↑

Fig.2 Accuracy and AUC values of MD model on two datasets

Fig.3 t-SNE visualization of some features in MD model

Fig.4 Variations of specificity and sensitivity of RMD model after using sample refinement strategy

References 44

[1]	PASHOUTAN S, SHOKOUHI S B, PASHOUTAN M. Automatic breast tumor classification using a level set method and feature extraction in mammography[C]//Proceedings of the 2017 24th National and 2nd International Iranian Conference on Biomedical Engineering, Tehran, Nov 30-Dec 1,2017. Piscataway: IEEE, 2017: 1-6.
[2]	WU E, WU K, COX D D, et al. Conditional infilling GANs for data augmentation in mammogram classification[C]//LNCS 11040: Proceedings of the 3rd International Workshop on Image Analysis for Moving Organ, Breast, and Thoracic Images, Granada, Sep 16-20, 2018. Cham: Springer, 2018: 98-106.
[3]	RADFORD A, METZ L, CHINTALA S. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. arXiv:1511.06434, 2015.
[4]	LI H Y, CHEN D D, NAILON W H, et al. A deep dual-path network for improved mammogram image processing[C]//Proceedings of the 2019 International Conference on Acoustics, Speech and Signal Processing, Brighton, May 12-17, 2019. Piscataway: IEEE, 2019: 1224-1228.
[5]	RIBLI D, HORVÁTH A, UNGER Z, et al. Detecting and classifying lesions in mammograms with deep learning[J]. Scientific Reports, 2018, 8(1):4165. DOI URL
[6]	HAGHIGHAT M, ABDEL-MOTTALEB M, ALHALABI W. Discriminant correlation analysis: real-time feature level fusion for multimodal biometric recognition[J]. IEEE Transactions on Information Forensics and Security, 2016, 11(9):1984-1996. DOI URL
[7]	LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2):91-110. DOI URL
[8]	O’ROURKE S M, HERSKOWITZ I, O’SHEA E K. Yeast go the whole HOG for the hyperosmotic response[J]. Trends in Genetics, 2002, 18(8):405-412. DOI URL
[9]	LI Y, CHEN H, WEI X, et al. Mass classification in mam- mograms based on two-concentric masks and discriminating texton[J]. Pattern Recognition, 2016, 60:648-656. DOI URL
[10]	LIU X, TANG J. Mass classification in mammograms using selected geometry and texture features, and a new SVM-based feature selection method[J]. IEEE Systems Journal, 2013, 8(3):910-920. DOI URL
[11]	GUO Z, ZHANG L, ZHANG D. A completed modeling of local binary pattern operator for texture classification[J]. IEEE Transactions on Image Processing, 2010, 19(6):1657-1663. DOI URL
[12]	HARALICK R M, SHANMUGAM K. Textural features for image classification[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1973, 6:610-621.
[13]	JI H K, GUO K W, YANG L Z, et al. Research on feature selection and classification algorithm of medical optical tomography images[C]//Proceedings of the 2019 IEEE/CIC International Conference on Communications in China,Changchun, Aug 11-13, 2019. Piscataway: IEEE, 2019: 561-566.
[14]	VEERAMUTHU A, MEENAKSHI S, KAMESHWRAN A. A plug-in feature extraction and feature subset selection algorithm for classification of medicinal brain image data[C]//Proceedings of the 2014 International Conference on Communication and Signal Processing, Melmaruvathur, Apr 3-5, 2014. Piscataway: IEEE, 2014: 1545-1551.
[15]	KUMAR S U, INBARANI H H. PSO-based feature selection and neighborhood rough set-based classification for BCI multiclass motor imagery task[J]. Neural Computing and Applications, 2017, 28(11):3239-3258. DOI URL
[16]	SUDHA M N, SELVARAJAN S, SUGANTHI M. Feature selection using improved lion optimisation algorithm for breast cancer classification[J]. International Journal of Bio-Inspired Computation, 2019, 14(4):237-246. DOI URL
[17]	ZHU X, SUK H I, LEE S W, et al. Subspace regularized sparse multitask learning for multiclass neurodegenerative disease identification[J]. IEEE Transactions on Biomedical Engineering, 2015, 63(3):607-618. DOI URL
[18]	ZHANG D, SHEN D. Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease[J]. NeuroImage, 2012, 59(2):895-907. DOI URL
[19]	ZHOU T, LIU M, THUNG K H, et al. Latent representation learning for Alzheimer’s disease diagnosis with incomplete multi-modality neuroimaging and genetic data[J]. IEEE Transactions on Medical Imaging, 2019, 38(10):2411-2422. DOI URL
[20]	ZHENG X, SHI J, LI Y, et al. Multi-modality stacked deep polynomial network-based feature learning for Alzheimer’s disease diagnosis[C]//Proceedings of the 13th IEEE International Symposium on Biomedical Imaging, Prague, Apr 13-16, 2016. Piscataway: IEEE, 2016: 851-854.
[21]	SHAO W, XIANG S N, ZHANG Z Y, et al. Hyper-graph based sparse canonical correlation analysis for the diagnosis of Alzheimer’s disease from multi-dimensional genomic data[J]. Methods, 2021, 189:86-94. DOI URL
[22]	LIU M, GAO Y, YAP P T, et al. Multi-hypergraph learning for incomplete multimodality data[J]. IEEE Journal of Biomedical and Health Informatics, 2017, 22(4):1197-1208. DOI URL
[23]	AGARWAL R, DIAZ O, LLADÓ X, et al. Automatic mass detection in mammograms using deep convolutional neural networks[J]. Journal of Medical Imaging, 2019, 6(3):031409.
[24]	TSOCHATZIDIS L, COSTARIDOU L, PRATIKAKIS I. Deep learning for breast cancer diagnosis from mammograms—a comparative study[J]. Journal of Imaging, 2019, 5(3):37. DOI URL
[25]	RAGAB D A, SHARKAS M, MARSHALL S, et al. Breast cancer detection using deep convolutional neural networks and support vector machines[J]. PeerJ, 2019, 7:e6201. DOI URL
[26]	DHUNGEL N, CARNEIRO G, BRADLEY A P. Fully automated classification of mammograms using deep residual neural networks[C]//Proceedings of the 14th IEEE International Symposium on Biomedical Imaging, Melbourne, Apr 18-21, 2017. Piscataway: IEEE, 2017: 310-314.
[27]	RAMPUN A, SCOTNEY B W, MORROW P J, et al. Breast mass classification in mammograms using ensemble convolutional neural networks[C]//Proceedings of the 20th IEEE International Conference on e-Health Networking, Applications and Services, Ostrava, Sep 17-20, 2018. Piscataway: IEEE, 2018: 1-6.
[28]	KHAN S U, ISLAM N, JAN Z, et al. A novel deep learning based framework for the detection and classification of breast cancer using transfer learning[J]. Pattern Recognition Letters, 2019, 125:1-6. DOI URL
[29]	SHEN L, MARGOLIES L R, ROTHSTEIN J H, et al. Deep learning to improve breast cancer detection on screening mammography[J]. Scientific Reports, 2019, 9(1):1-12.
[30]	BYRA M, GALPERIN M, OJEDA-FOURNIER H, et al. Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion[J]. Medical Physics, 2019, 46(2):746-755. DOI URL
[31]	ANSAR W, SHAHID A R, RAZA B, et al. Breast cancer detection and localization using MobileNet based transfer learning for mammograms[C]//Proceedings of the 2020 International Symposium on Intelligent Computing Systems, Sharjah, Mar 18-19, 2020. Cham: Springer, 2020: 11-21.
[32]	WANG K, PATEL B K, WANG L, et al. A dual-mode deep transfer learning (D2TL) system for breast cancer detection using contrast enhanced digital mammograms[J]. IISE Transactions on Healthcare Systems Engineering, 2019, 9(4):357-370. DOI URL
[33]	HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Jul 21-26, 2017. Washington: IEEE Computer Society, 2017: 2261-2269.
[34]	HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, Jun 27-30, 2016. Washington: IEEE Computer Society, 2016: 770-778.
[35]	SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. arXiv:1409. 1556, 2014.
[36]	余绍德. 卷积神经网络和迁移学习在癌症影像分析中的研究[D]. 深圳: 中国科学院大学(中国科学院深圳先进技术研究院), 2018.
	YU S D. Research on convolutional neural network and transfer learning in cancer image analysis[D]. Shenzhen: University of Chinese Academy of Sciences (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences), 2018.
[37]	ZHANG H, QIU D, WU R, et al. Novel framework for image attribute annotation with gene selection XGBoost algorithm and relative attribute model[J]. Applied Soft Computing, 2019, 80:57-79. DOI URL
[38]	LIN X H, SONG H H, FAN M, et al. The feature selection algorithm based on feature overlapping and group overlapping[C]//Proceedings of the 2016 IEEE International Conference on Bioinformatics and Biomedicine, Shenzhen, Dec 15-18, 2016. Washington: IEEE Computer Society, 2016: 619-624.
[39]	LI J D, CHENG K W, WANG S H, et al. Feature selection: a data perspective[J]. ACM Computing Surveys, 2017, 50(6):1-45.
[40]	ZHU W T, LOU Q, VANG Y S, et al. Deep multi-instance networks with sparse label assignment for whole mammogram classification[C]//LNCS 10435: Proceedings of the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, Quebec City, Sep 11-13, 2017. Cham: Springer, 2017: 603-611.
[41]	CARNEIRO G, NASCIMENTO J, BRADLEY A P. Deep learning models for classifying mammogram exams containing unregistered multi-view images and segmentation maps of lesions[J]. Deep Learning for Medical Image Analysis, 2017: 321-339.
[42]	DHUNGEL N, CARNEIRO G, BRADLEY A P. The automated learning of deep features for breast mass classification from mammograms[C]//LNCS 9901: Proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, Athens, Oct 17-21, 2016. Cham: Springer, 2016: 106-114.
[43]	WANG X L, ROSS G, ABHINAV G, et al. Non-local neural networks[C]//Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, Jun 18-22, 2018. Washington: IEEE Computer Society, 2018: 7794-7803.
[44]	SHABAN W M, RABIE A H, SALEH A I, et al. A new COVID-19 patients detection strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier[J]. Knowledge-Based Systems, 2020, 205:106270. DOI URL

Breast Mass Recognition Model via Deep-Level Pathological Information Mining

融入深层病理信息挖掘的乳腺肿块识别模型

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