Computerized Analysis of Mammographic Parenchymal Patterns on a Large Clinical Dataset of Full-Field Digital Mammograms: Robustness Study with Two High-Risk Datasets-Reference-Cited by-同舟云学术

Computerized Analysis of Mammographic Parenchymal Patterns on a Large Clinical Dataset of Full-Field Digital Mammograms: Robustness Study with Two High-Risk Datasets

Published:2012-01-13 Issue:5 Volume:25 Page:591-598
ISSN:0897-1889
Container-title:Journal of Digital Imaging
language:en
Short-container-title:J Digit Imaging

Author:

Li Hui,Giger Maryellen L.,Lan Li,Bancroft Brown Jeremy,MacMahon Aoife,Mussman Mary,Olopade Olufunmilayo I.,Sennett Charlene

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Link

http://link.springer.com/content/pdf/10.1007/s10278-012-9452-z.pdf

Reference31 articles.

1. Siegel R, Ward E, Brawley O, Jemal A: Cancer Statistics, 2011: the impact of eliminating socioeconomic and racial disparities on premature cancer deaths. CA Cancer J Clin 61:212–236, 2011

2. American College of Radiology: Breast Imaging Reporting and Data System (BI-RADS), 4th edition. American College of Radiology, Reston, Va, 2003

3. Wolfe JN: Breast patterns as an index of risk for developing breast cancer. Am J Roentgenol 126:1130–1139, 1976

4. Boyd NF, Martin LJ, Stone J, Greenberg, et al: Mammographic densities as a marker of human breast cancer risk and their use in chemoprevention. Curr Oncol Rep 3:314–321, 2001

5. Brisson J, Diorio C, Mâsse B: Wolfe’s parenchymal pattern and percentage of the breast with mammographic densities: redundant or complementary classifications? Cancer Epidemiol Biomarkers Prev 12:728–732, 2003

Cited by 41 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. A machine learning-based approach to identify peripheral artery disease using texture features from contrast-enhanced magnetic resonance imaging;Magnetic Resonance Imaging;2024-02

2. Mammographic breast density segmentation;Artificial Intelligence and Image Processing in Medical Imaging;2024

3. Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers;Biochimica et Biophysica Acta (BBA) - Reviews on Cancer;2023-11

4. Avoiding Tissue Overlap in 2D Images: Single-Slice DBT Classification Using Convolutional Neural Networks;Tomography;2023-02-14

5. Application of a Texture Appearance Model for Segmentation of Lung Nodules on Computed Tomography of the Chest;Journal of the Russian Universities. Radioelectronics;2022-06-28