First results with a deep learning (feed-forward CNN) approach for daily quality control in digital breast tomosynthesis

2018 ◽  
Author(s):  
Dimitar Petrov ◽  
Hilde Bosmans ◽  
Nicholas Marshall ◽  
Lesley Cockmartin
Keyword(s):  
Quality Control ◽  
Deep Learning ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis ◽  
Feed Forward ◽  
First Results

scholarly journals A deep learning classifier for digital breast tomosynthesis

2021 ◽  
Vol 83 ◽  
pp. 184-193
Author(s):  
R. Ricciardi ◽  
G. Mettivier ◽  
M. Staffa ◽  
A. Sarno ◽  
G. Acampora ◽  
...  
Keyword(s):  
Deep Learning ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis ◽  
Learning Classifier

scholarly journals Lesion Detection in Breast Tomosynthesis Using Efficient Deep Learning and Data Augmentation Techniques

2021 ◽  
Author(s):  
Loay Hassan ◽  
Mohamed Abedl-Nasser ◽  
Adel Saleh ◽  
Domenec Puig
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Breast Lesion ◽  
Data Augmentation ◽  
Digital Breast Tomosynthesis ◽  
Lesion Detection ◽  
Detection Methods ◽  
Breast Tomosynthesis ◽  
Mammographic Images ◽  
Augmentation Techniques

Digital breast tomosynthesis (DBT) is one of the powerful breast cancer screening technologies. DBT can improve the ability of radiologists to detect breast cancer, especially in the case of dense breasts, where it beats mammography. Although many automated methods were proposed to detect breast lesions in mammographic images, very few methods were proposed for DBT due to the unavailability of enough annotated DBT images for training object detectors. In this paper, we present fully automated deep-learning breast lesion detection methods. Specifically, we study the effectiveness of two data augmentation techniques (channel replication and channel-concatenation) with five state-of-the-art deep learning detection models. Our preliminary results on a challenging publically available DBT dataset showed that the channel-concatenation data augmentation technique can significantly improve the breast lesion detection results for deep learning-based breast lesion detectors.

Phantoms for Quality Control Procedures of Digital Breast Tomosynthesis

2012 ◽  
pp. 322-329
Author(s):  
Ramona W. Bouwman ◽  
Oliver Diaz ◽  
Kenneth C. Young ◽  
Ruben E. van Engen ◽  
Wouter J. H. Veldkamp ◽  
...  
Keyword(s):  
Quality Control ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis ◽  
Control Procedures
Sign in / Sign up

Export Citation Format

Share Document