scholarly journals Automated mammographic breast density estimation using a fully convolutional network

2018 ◽  
Vol 45 (3) ◽  
pp. 1178-1190 ◽  
Author(s):  
Juhun Lee ◽  
Robert M. Nishikawa
Keyword(s):  
Density Estimation ◽  
Breast Density ◽  
Mammographic Breast Density ◽  
Convolutional Network ◽  
Fully Convolutional Network

Dependence of radiation dose on area and volumetric mammographic breast density estimation

2013 ◽  
Author(s):  
H. Jing ◽  
B. Keller ◽  
Jae Young Choi ◽  
R. Crescenzi ◽  
E. Conant ◽  
...  
Keyword(s):  
Radiation Dose ◽  
Density Estimation ◽  
Breast Density ◽  
Mammographic Breast Density

scholarly journals MaskNet: A Fully-Convolutional Network to Estimate Inlier Points

2020 ◽  
Author(s):  
Vinit Sarode ◽  
Animesh Dhagat ◽  
Rangaprasad Arun Srivatsan ◽  
Nicolas Zevallos ◽  
Simon Lucey ◽  
...  
Keyword(s):  
Convolutional Network ◽  
Fully Convolutional Network

scholarly journals Relationship of mammographic densities to breast cancer risk

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Engy A. Ali ◽  
Mariam Raafat
Keyword(s):  
Breast Cancer ◽  
Breast Density ◽  
Statistical Significance ◽  
Significant Risk ◽  
Close Relation ◽  
Mammographic Screening ◽  
Mammographic Breast Density ◽  
Study Results ◽  
Relationship Of ◽  
Mammographic Densities

Abstract Background Our goal was to find out the relation between mammographic densities and cancer of the breast according to the recent ACR classification. From the medical records of Kasereliny Hospital, 49,409 women were subjected to digital mammography for screening, of which 1500 breast cancer cases were collected. The mammographic categories of breast density were ACR-A, B, C, and D, which were detected by two senior radiologists. All radiological classifications were made using both standard mammographic views bilaterally. Two-sided tests of statistical significance were represented by all the P values. Results From 2014 to 2019, 49,409 women came for digital mammographic screening, their age ranges between 40 and 65, and all of them are included in the study. One thousand cases of breast cancer cases were radiologically and pathologically diagnosed. Different densities were arranged in descending pattern depending on the frequency of positive cases: D (13.7%), C (3.3%), B (2.7%), A (2.2%). There is positive significant risk ratio among every higher mammographic density in comparison to the lower density. Conclusion Our study results show that the risk of breast cancer is in close relation to the mammographic breast density.

scholarly journals Parallel Fully Convolutional Network for Semantic Segmentation

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 673-682
Author(s):  
Jian Ji ◽  
Xiaocong Lu ◽  
Mai Luo ◽  
Minghui Yin ◽  
Qiguang Miao ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Convolutional Network ◽  
Fully Convolutional Network
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