Deep-learning convolution neural network for computer-aided detection of microcalcifications in digital breast tomosynthesis

2016 ◽  
Author(s):  
Ravi K. Samala ◽  
Heang-Ping Chan ◽  
Lubomir M. Hadjiiski ◽  
Kenny Cha ◽  
Mark A. Helvie
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Digital Breast Tomosynthesis ◽  
Convolution Neural Network ◽  
Computer Aided Detection ◽  
Breast Tomosynthesis ◽  
Computer Aided

Computer-Aided Detection and Diagnosis for 3D X-Ray Based Breast Imaging

2012 ◽  
pp. 66-85
Author(s):  
Gautam S. Muralidhar ◽  
Alan C. Bovik ◽  
Mia K. Markey
Keyword(s):  
Breast Imaging ◽  
Digital Breast Tomosynthesis ◽  
Future Research ◽  
Computer Aided Detection ◽  
Breast Tomosynthesis ◽  
X Ray ◽  
Computer Aided ◽  
Recent Developments ◽  
Initial Work ◽  
Detection And Diagnosis

The last 15 years has seen the advent of a variety of powerful 3D x-ray based breast imaging modalities such as digital breast tomosynthesis, digital breast computed tomography, and stereo mammography. These modalities promise to herald a new and exciting future for early detection and diagnosis of breast cancer. In this chapter, the authors review some of the recent developments in 3D x-ray based breast imaging. They also review some of the initial work in the area of computer-aided detection and diagnosis for 3D x-ray based breast imaging. The chapter concludes by discussing future research directions in 3D computer-aided detection.

scholarly journals Comparison Between Feature-Based and Convolutional Neural Network–Based Computer-Aided Diagnosis for Breast Cancer Classification in Digital Breast Tomosynthesis

2019 ◽  
pp. 1-18
Author(s):  
Siwa Chan ◽  
Jinn-Yi Yeh
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Digital Breast Tomosynthesis ◽  
Cancer Classification ◽  
Breast Tomosynthesis ◽  
Breast Cancer Classification ◽  
Computer Aided ◽  
Feature Based ◽  
Aided Diagnosis

Digital breast tomosynthesis (DBT) is a promising new technique for breast cancer diagnosis. DBT has the potential to overcome the tissue superimposition problems that occur on traditional mammograms for tumor detection. However, DBT generates numerous images, thereby creating a heavy workload for radiologists. Therefore, constructing an automatic computer-aided diagnosis (CAD) system for DBT image analysis is necessary. This study compared feature-based CAD and convolutional neural network (CNN)-based CAD for breast cancer classification from DBT images. The research methods included image preprocessing, candidate tumor identification, three-dimensional feature generation, classification, image cropping, augmentation, CNN model design, and deep learning. The precision rates (standard deviation) of the LeNet-based CNN CAD and the feature-based CAD for breast cancer classification were 89.84 (0.013) and 84.46 (0.082), respectively. The T value was -4.091 and the P value was 0.00 < 0.05, which indicate that the LeNet-based CNN CAD significantly outperform the feature-based CAD. However, there is no significantly differences between the LeNet-based CNN CAD and the feature-based CAD on other criteria. The results can be applied to clinical medicine and assist radiologists in breast cancer identification.

scholarly journals Computer-aided detection of clustered microcalcifications in digital breast tomosynthesis: A 3D approach

2011 ◽  
Vol 39 (1) ◽  
pp. 28-39 ◽  
Author(s):  
Berkman Sahiner ◽  
Heang-Ping Chan ◽  
Lubomir M. Hadjiiski ◽  
Mark A. Helvie ◽  
Jun Wei ◽  
...  
Keyword(s):  
Digital Breast Tomosynthesis ◽  
Computer Aided Detection ◽  
Breast Tomosynthesis ◽  
Computer Aided
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