Breast Fibroglandular Tissue Segmentation for Automated BPE Quantification With Iterative Cycle-Consistent Semi-Supervised Learning-Reference-Cited by-全球学者库

Breast Fibroglandular Tissue Segmentation for Automated BPE Quantification With Iterative Cycle-Consistent Semi-Supervised Learning

Published:2023-12 Issue:12 Volume:42 Page:3944-3955
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Zhang Jiadong¹^ORCID,Cui Zhiming¹,Zhou Luping²^ORCID,Sun Yiqun³,Li Zhenhui⁴^ORCID,Liu Zaiyi⁵^ORCID,Shen Dinggang¹^ORCID

Affiliation:

1. School of Biomedical Engineering, ShanghaiTech University, Shanghai, China

2. School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW, Australia

3. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China

4. Department of Radiology, Yunnan Cancer Hospital, Third Affiliated Hospital of Kunming Medical University, Kunming, China

5. Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangdong, China

Funder

Key Research and Development Program of Guangdong Province, China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/10336247/10265133.pdf?arnumber=10265133

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5. Are Qualitative Assessments of Background Parenchymal Enhancement, Amount of Fibroglandular Tissue on MR Images, and Mammographic Density Associated with Breast Cancer Risk?