Feature generation and multi-sequence fusion based deep convolutional network for breast tumor diagnosis with missing MR sequences
Author:
Funder
National Natural Science Foundation of China
Publisher
Elsevier BV
Subject
Health Informatics,Signal Processing,Biomedical Engineering
Reference46 articles.
1. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries;Sung;CA Cancer J. Clin.,2021
2. A deep learning fusion model with evidence-based confidence level analysis for differentiation of malignant and benign breast tumors using dynamic contrast enhanced MRI;Wu;Biomed. Signal Process. Control.,2022
3. The role of histogram analysis in diffusion-weighted imaging in the differential diagnosis of benign and malignant breast lesions;Jin;BMC Med. Inform. Decis. Mak.,2020
4. Diagnostic performance of whole-lesion apparent diffusion coefficient histogram analysis metrics for differentiating benign and malignant breast lesions: a systematic review and diagnostic meta-analysis;Xu;Acta Radiol.,2020
5. The value of dynamic contrast-enhanced magnetic resonance imaging combined with apparent diffusion coefficient in the differentiation of benign and malignant diseases of the breast;Ao;Acta Radiol.,2021
Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. B-mode ultrasound-based CAD by learning using privileged information with dual-level missing modality completion;Computers in Biology and Medicine;2024-11
2. Clinical study on improving the diagnostic accuracy of adult elbow joint cartilage injury by multisequence magnetic resonance imaging;World Journal of Clinical Cases;2024-09-06
3. Feature-enhanced multi-sequence MRI-based fusion mechanism for breast tumor segmentation;Biomedical Signal Processing and Control;2024-04
4. Efficiency of modern methods of examination of benign breast diseases in women;Health care of Tajikistan;2024-02-24
5. Reconstructing Missing Modalities in Multi-Modal Endoscopic Ultrasound Via Cross-Modal Feature Replacement Representation;2024
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3