Reproducibility of quantitative high-throughput BI-RADS features extracted from ultrasound images of breast cancer-Reference-Cited by-同舟云学术

Reproducibility of quantitative high-throughput BI-RADS features extracted from ultrasound images of breast cancer

Published:2017-05-16 Issue:7 Volume:44 Page:3676-3685
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Med. Phys.

Author:

Hu Yuzhou¹,Qiao Mengyun¹,Guo Yi²,Wang Yuanyuan²,Yu Jinhua²,Li Jiawei³,Chang Cai³

Affiliation:

1. Department of Electronic Engineering; Fudan University; Shanghai 200433 China

2. Department of Electronic Engineering; Fudan University and Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai; Shanghai 200433 China

3. Department of Ultrasound; Fudan University Shanghai Cancer Center; Shanghai 200032 China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

General Medicine

Reference36 articles.

1. Cancer statistics, 2015;Siegel;CA Cancer J Clin,2015

2. Cancer statistics in China, 2015;Chen;CA Cancer J Clin,2016

3. Detection of breast cancer with addition of annual screening ultrasound or a single screening MRI to mammography in women with elevated breast cancer risk;Berg;JAMA J Am Med Assoc,2012

4. Diagnosis of breast cancer: Contribution of US as an adjunct to mammography;Zonderland;Radiology,1999

5. Solid breast nodules: Use of sonography to distinguish between benign and malignant lesions;Stavros;Radiology,1995

Cited by 14 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. Machine Learning and Computer Vision Based Methods for Cancer Classification: A Systematic Review;Archives of Computational Methods in Engineering;2024-02-12

2. Boundary-Guided and Region-Aware Network With Global Scale-Adaptive for Accurate Segmentation of Breast Tumors in Ultrasound Images;IEEE Journal of Biomedical and Health Informatics;2023-09

3. Fusion of Human Cognitive Knowledge and Machine Inference for Breast Cancer Detection;2023 International Conference on Advanced Robotics and Mechatronics (ICARM);2023-07-08

4. Combined B-mode and Nakagami Images for Improved Discrimination of Breast Masses using Deep Learning;2022 IEEE International Ultrasonics Symposium (IUS);2022-10-10

5. BGRA-Net: Boundary-Guided and Region-Aware Convolutional Neural Network for the Segmentation of Breast Ultrasound Images;2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2021-12-09