IRUM: An Image Representation and Unified Learning Method for Breast Cancer Diagnosis from Multi-View Ultrasound Images

doi:10.1007/978-3-031-73284-3_3

	IRUM: An Image Representation and Unified Learning Method for Breast Cancer Diagnosis from Multi-View Ultrasound Images
	Chen, Haoyuan1 ; Li, Yonghao1 ; Zhang, Jiadong1,2 ; Xu, Qi 3; Li, Meiyu1 ; Li, Zhenhui 4; Qian, Xuejun1 ; Shen, Dinggang1,5,6
	2025
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	15241 LNCS
页码	22-30
发表状态	已发表
DOI	10.1007/978-3-031-73284-3_3
摘要	Multi-view breast ultrasound imaging has been routinely performed in clinical settings to ensure comprehensive disease evaluation. Recently, artificial intelligence (AI) has been developed to interpret medical images; however, most of the current AI models are restricted to single-view images, resulting in weak representation of breast 3D tissues. Here, we develop an Image Representation and Unified learning Method (IRUM) on a dataset comprising 3800 ultrasound images from 1900 patients with an accuracy of 86.8%. Owing to the design of four distinct learning modules, the proposed IRUM is not only able to predict breast cancer risk using multi-view inputs, but also compatible with single-view input (a commonly encountered situation in clinical practice). We demonstrate that the IRUM achieves superior performance to conventional single-view and multi-view approaches to a certain degree. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
关键词	Contrastive Learning Lung cancer Medical imaging Ultrasonic imaging Breast Cancer Breast cancer diagnosis Breast ultrasound imaging Clinical settings Consistency learning Image representations Learning methods Multi-view diagnose Multi-views Ultrasound images
会议名称	15th International Workshop on Machine Learning in Medical Imaging, MLMI 2024 was held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024
出版地	GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
会议地点	Marrakesh, Morocco
会议日期	October 6, 2024 - October 6, 2024
URL	查看原文
收录类别	EI ; CPCI-S
语种	英语
资助项目	National Natural Science Foundation of China["62131015","62250710165","U23A20295","2022ZD0209000"] ; Shanghai Municipal Central Guided Local Science and Technology Development Fund[YDZX20233100001001] ; Key R&D Program of Guangdong Province, China["2023B0303040001","2021B0101420006"]
WOS研究方向	Computer Science ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001424557900003
出版者	Springer Science and Business Media Deutschland GmbH
EI入藏号	20244517332428
EI主题词	Image representation
EISSN	1611-3349
EI分类号	101.1 ; 102.1.1 ; 1101.2 ; 1106.3.1 ; 746 Imaging Techniques ; 753.3 Ultrasonic Applications
原始文献类型	Conference article (CA)
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/449152
专题	生物医学工程学院_博士生信息科学与技术学院_硕士生信息科学与技术学院_博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_PI研究组_钱学骏组
通讯作者	Shen, Dinggang
作者单位	1.School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai; 201210, China 2.Department of Biomedical Engineering, City University of Hong Kong, 999077, Hong Kong 3.Department of Ultrasound, Wenzhou People’s Hospital, Wenzhou; 325000, China 4.Department of Radiology, Yunnan Cancer Hospital, Kunming; 650118, China 5.Shanghai United Imaging Intelligence Co., Ltd., Shanghai; 200230, China 6.Shanghai Clinical Research and Trial Center, Shanghai; 201210, China
第一作者单位	上海科技大学
通讯作者单位	上海科技大学
第一作者的第一单位	上海科技大学
推荐引用方式 GB/T 7714	Chen, Haoyuan,Li, Yonghao,Zhang, Jiadong,et al. IRUM: An Image Representation and Unified Learning Method for Breast Cancer Diagnosis from Multi-View Ultrasound Images[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:Springer Science and Business Media Deutschland GmbH,2025:22-30.