A novel deep learning-based approach to high accuracy breast density estimation in digital mammography

2017 ◽  
Author(s):  
Chul Kyun Ahn ◽  
Changyong Heo ◽  
Heongmin Jin ◽  
Jong Hyo Kim
Keyword(s):  
Deep Learning ◽  
Density Estimation ◽  
Breast Density ◽  
Digital Mammography ◽  
High Accuracy

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

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 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 The Impact of Acquisition Dose on Quantitative Breast Density Estimation with Digital Mammography: Results from ACRIN PA 4006

Radiology ◽  
2016 ◽  
Vol 280 (3) ◽  
pp. 693-700
Author(s):  
Lin Chen ◽  
Shonket Ray ◽  
Brad M. Keller ◽  
Said Pertuz ◽  
Elizabeth S. McDonald ◽  
...  
Keyword(s):  
Density Estimation ◽  
Breast Density ◽  
Digital Mammography ◽  
The Impact

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 Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

Sign in / Sign up

Export Citation Format

Share Document