Volumetric breast density estimation on MRI using explainable deep learning regression
出版年份 2020 全文链接
标题
Volumetric breast density estimation on MRI using explainable deep learning regression
作者
关键词
-
出版物
Scientific Reports
Volume 10, Issue 1, Pages -
出版商
Springer Science and Business Media LLC
发表日期
2020-10-22
DOI
10.1038/s41598-020-75167-6
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- A deep learning framework for efficient analysis of breast volume and fibroglandular tissue using MR data with strong artifacts
- (2019) Tatyana Ivanovska et al. International Journal of Computer Assisted Radiology and Surgery
- Automatic Breast and Fibroglandular Tissue Segmentation in Breast MRI Using Deep Learning by a Fully-Convolutional Residual Neural Network U-Net
- (2019) Yang Zhang et al. ACADEMIC RADIOLOGY
- An automated approach for the optimised estimation of breast density with Dixon methods
- (2019) Rosie Goodburn et al. BRITISH JOURNAL OF RADIOLOGY
- Quantitative breast density analysis using tomosynthesis and comparison with MRI and digital mammography
- (2018) Woo Kyung Moon et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- Reproducible automated breast density measure with no ionizing radiation using fat-water decomposition MRI
- (2018) Jie Ding et al. JOURNAL OF MAGNETIC RESONANCE IMAGING
- Fully Automated Convolutional Neural Network Method for Quantification of Breast MRI Fibroglandular Tissue and Background Parenchymal Enhancement
- (2018) Richard Ha et al. JOURNAL OF DIGITAL IMAGING
- Using deep learning to segment breast and fibroglandular tissue in MRI volumes
- (2017) Mehmet Ufuk Dalmış et al. MEDICAL PHYSICS
- Breast Density and Benign Breast Disease: Risk Assessment to Identify Women at High Risk of Breast Cancer
- (2015) Jeffrey A. Tice et al. JOURNAL OF CLINICAL ONCOLOGY
- Association between Parenchymal Enhancement of the Contralateral Breast in Dynamic Contrast-enhanced MR Imaging and Outcome of Patients with Unilateral Invasive Breast Cancer
- (2015) Bas H. M. van der Velden et al. RADIOLOGY
- Breast Segmentation and Density Estimation in Breast MRI: A Fully Automatic Framework
- (2015) Albert Gubern-Merida et al. IEEE Journal of Biomedical and Health Informatics
- Volumetric Breast Density Estimation from Full-Field Digital Mammograms: A Validation Study
- (2014) Albert Gubern-Mérida et al. PLoS One
- Using Clinical Factors and Mammographic Breast Density to Estimate Breast Cancer Risk: Development and Validation of a New Predictive Model
- (2013) Jeffrey A. Tice et al. ANNALS OF INTERNAL MEDICINE
- World Medical Association Declaration of Helsinki
- (2013) JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas-aided fuzzy C-means method
- (2013) Shandong Wu et al. MEDICAL PHYSICS
- Agreement of Mammographic Measures of Volumetric Breast Density to MRI
- (2013) Jeff Wang et al. PLoS One
- Dense Breast Legislation in the United States: State of the States
- (2013) Soudabeh Fazeli Dehkordy et al. Journal of the American College of Radiology
- A practical approach to manage additional lesions at preoperative breast MRI in patients eligible for breast conserving therapy: results
- (2010) Lotte E. Elshof et al. BREAST CANCER RESEARCH AND TREATMENT
- N4ITK: Improved N3 Bias Correction
- (2010) Nicholas J Tustison et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Breast Tissue Composition and Susceptibility to Breast Cancer
- (2010) N. F. Boyd et al. JNCI-Journal of the National Cancer Institute
- The impact of preoperative MRI on breast-conserving surgery of invasive cancer: a comparative cohort study
- (2008) K. E. Pengel et al. BREAST CANCER RESEARCH AND TREATMENT
- Development of a quantitative method for analysis of breast density based on three-dimensional breast MRI
- (2008) Ke Nie et al. MEDICAL PHYSICS
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started