A deep learning method for volumetric breast density estimation from processed full field digital mammograms

2019 ◽  
Author(s):  
Michiel Kallenberg ◽  
Doiriel Vanegas Camargo ◽  
Mahlet Birhanu ◽  
Albert Gubern-Mérida ◽  
Nico Karssemeijer
Keyword(s):  
Deep Learning ◽  
Density Estimation ◽  
Breast Density ◽  
Learning Method ◽  
Full Field

scholarly journals A Multisite Study of a Breast Density Deep Learning Model for Full-Field Digital Mammography and Synthetic Mammography

2021 ◽  
Vol 3 (1) ◽  
pp. e200015
Author(s):  
Thomas P. Matthews ◽  
Sadanand Singh ◽  
Brent Mombourquette ◽  
Jason Su ◽  
Meet P. Shah ◽  
...  
Keyword(s):  
Deep Learning ◽  
Breast Density ◽  
Digital Mammography ◽  
Learning Model ◽  
Full Field ◽  
Full Field Digital Mammography ◽  
Deep Learning Model

scholarly journals Volumetric Breast Density Estimation from Full-Field Digital Mammograms: A Validation Study

PLoS ONE ◽  
2014 ◽  
Vol 9 (1) ◽  
pp. e85952 ◽  
Author(s):  
Albert Gubern-Mérida ◽  
Michiel Kallenberg ◽  
Bram Platel ◽  
Ritse M. Mann ◽  
Robert Martí ◽  
...  
Keyword(s):  
Density Estimation ◽  
Breast Density ◽  
Validation Study ◽  
Full Field

Volumetric breast density estimation from full-field digital mammograms

2006 ◽  
Vol 25 (3) ◽  
pp. 273-282 ◽  
Author(s):  
S. van Engeland ◽  
P.R. Snoeren ◽  
H. Huisman ◽  
C. Boetes ◽  
N. Karssemeijer
Keyword(s):  
Density Estimation ◽  
Breast Density ◽  
Full Field

scholarly journals Interpretable deep learning regression for breast density estimation on MRI

2020 ◽  
Author(s):  
Bas H.M. . van der Velden ◽  
Max A. A. Ragusi ◽  
Markus H. A. Janse ◽  
Claudette E. Loo ◽  
Kenneth G. A. Gilhuijs
Keyword(s):  
Deep Learning ◽  
Density Estimation ◽  
Breast Density

scholarly journals A deep learning method for classifying mammographic breast density categories

2017 ◽  
Vol 45 (1) ◽  
pp. 314-321 ◽  
Author(s):  
Aly A. Mohamed ◽  
Wendie A. Berg ◽  
Hong Peng ◽  
Yahong Luo ◽  
Rachel C. Jankowitz ◽  
...  
Keyword(s):  
Deep Learning ◽  
Breast Density ◽  
Mammographic Breast Density ◽  
Learning Method

scholarly journals Fully Automated Breast Density Segmentation and Classification Using Deep Learning

Diagnostics ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 988
Author(s):  
Nasibeh Saffari ◽  
Hatem A. Rashwan ◽  
Mohamed Abdel-Nasser ◽  
Vivek Kumar Singh ◽  
Meritxell Arenas ◽  
...  
Keyword(s):  
Deep Learning ◽  
Density Estimation ◽  
Breast Density ◽  
Breast Imaging ◽  
Screening Mammography ◽  
Generative Adversarial Networks ◽  
Fatty Tissue ◽  
Tissue Segmentation ◽  
Visual Evaluation ◽  
Low Contrast

Breast density estimation with visual evaluation is still challenging due to low contrast and significant fluctuations in the mammograms’ fatty tissue background. The primary key to breast density classification is to detect the dense tissues in the mammographic images correctly. Many methods have been proposed for breast density estimation; nevertheless, most of them are not fully automated. Besides, they have been badly affected by low signal-to-noise ratio and variability of density in appearance and texture. This study intends to develop a fully automated and digitalized breast tissue segmentation and classification using advanced deep learning techniques. The conditional Generative Adversarial Networks (cGAN) network is applied to segment the dense tissues in mammograms. To have a complete system for breast density classification, we propose a Convolutional Neural Network (CNN) to classify mammograms based on the standardization of Breast Imaging-Reporting and Data System (BI-RADS). The classification network is fed by the segmented masks of dense tissues generated by the cGAN network. For screening mammography, 410 images of 115 patients from the INbreast dataset were used. The proposed framework can segment the dense regions with an accuracy, Dice coefficient, Jaccard index of 98%, 88%, and 78%, respectively. Furthermore, we obtained precision, sensitivity, and specificity of 97.85%, 97.85%, and 99.28%, respectively, for breast density classification. This study’s findings are promising and show that the proposed deep learning-based techniques can produce a clinically useful computer-aided tool for breast density analysis by digital mammography.

scholarly journals Reader Variability in Breast Density Estimation from Full-Field Digital Mammograms

2013 ◽  
Vol 20 (5) ◽  
pp. 560-568 ◽  
Author(s):  
Brad M. Keller ◽  
Diane L. Nathan ◽  
Sara C. Gavenonis ◽  
Jinbo Chen ◽  
Emily F. Conant ◽  
...  
Keyword(s):  
Density Estimation ◽  
Breast Density ◽  
Full Field

scholarly journals A Deep-Learning Approach to Recreate Raw Full-Field Digital Mammograms for Breast Density and Texture Analysis

2021 ◽  
pp. e200097
Author(s):  
Hai Shu ◽  
Tingyu Chiang ◽  
Peng Wei ◽  
Kim-Anh Do ◽  
Michele D. Lesslie ◽  
...  
Keyword(s):  
Deep Learning ◽  
Texture Analysis ◽  
Breast Density ◽  
Learning Approach ◽  
Full Field
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