Mammo-Net: Integrating Gaze Supervision and Interactive Information in Multi-view Mammogram Classification

doi:10.1007/978-3-031-43990-2_7

	Mammo-Net: Integrating Gaze Supervision and Interactive Information in Multi-view Mammogram Classification
	Ji, Changkai1,2 ; Du, Changde 2; Zhang, Qing 3; Wang, Sheng 1,4,5; Ma, Chong 6; Xie, Jiaming 7; Zhou, Yan 3; He, Huiguang1,2 ; Shen, Dinggang1,5,8
	2023
会议录名称	LECTURE NOTES IN COMPUTER SCIENCE (INCLUDING SUBSERIES LECTURE NOTES IN ARTIFICIAL INTELLIGENCE AND LECTURE NOTES IN BIOINFORMATICS)
ISSN	0302-9743
卷号	14226 LNCS
页码	68-78
DOI	10.1007/978-3-031-43990-2_7
摘要	Breast cancer diagnosis is a challenging task. Recently, the application of deep learning techniques to breast cancer diagnosis has become a popular trend. However, the effectiveness of deep neural networks is often limited by the lack of interpretability and the need for significant amount of manual annotations. To address these issues, we present a novel approach by leveraging both gaze data and multi-view data for mammogram classification. The gaze data of the radiologist serves as a low-cost and simple form of coarse annotation, which can provide rough localizations of lesions. We also develop a pyramid loss better fitting to the gaze-supervised process. Moreover, considering many studies overlooking interactive information relevant to diagnosis, we accordingly utilize transformer-based attention in our network to mutualize multi-view pathological information, and further employ a bidirectional fusion learning (BFL) to more effectively fuse multi-view information. Experimental results demonstrate that our proposed model significantly improves both mammogram classification performance and interpretability through incorporation of gaze data and cross-view interactive information. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
关键词	Classification (of information) Computer aided diagnosis Diseases Learning systems Mammography X ray screens Bidirectional fusion learning Breast cancer diagnosis Gaze Interactive informations Interpretability Learning techniques Mammogram classifications Manual annotation Multi-view interaction Multi-views
会议名称	26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023
会议地点	Vancouver, BC, Canada
会议日期	October 8, 2023 - October 12, 2023
收录类别	EI
语种	英语
出版者	Springer Science and Business Media Deutschland GmbH
EI入藏号	20234314954958
EI主题词	Deep neural networks
EISSN	1611-3349
EI分类号	461.1 Biomedical Engineering ; 461.4 Ergonomics and Human Factors Engineering ; 461.7 Health Care ; 716.1 Information Theory and Signal Processing ; 723.5 Computer Applications ; 746 Imaging Techniques ; 903.1 Information Sources and Analysis
原始文献类型	Conference article (CA)
引用统计	正在获取...
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/345830
专题	生物医学工程学院信息科学与技术学院_硕士生生物医学工程学院_PI研究组_沈定刚组生物医学工程学院_特聘教授组_何晖光组
通讯作者	He, Huiguang
作者单位	1.School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; 2.State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China; 3.Department of Radiology, Renji Hospital Shanghai Jiao Tong University School of Medicine, Shanghai, China; 4.Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China; 5.Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; 6.School of Automation, Northwestern Polytechnical University, Xi’an, China; 7.Department of Computer Science, The University of Hong Kong, Hong Kong; 8.Shanghai Clinical Research and Trial Center, Shanghai, China
第一作者单位	生物医学工程学院
通讯作者单位	生物医学工程学院
第一作者的第一单位	生物医学工程学院
推荐引用方式 GB/T 7714	Ji, Changkai,Du, Changde,Zhang, Qing,et al. Mammo-Net: Integrating Gaze Supervision and Interactive Information in Multi-view Mammogram Classification[C]:Springer Science and Business Media Deutschland GmbH,2023:68-78.