Shape-based breast lesion classification using digital tomosynthesis images: The role of explainable artificial intelligence

SM Hussain, D Buongiorno, N Altini, F Berloco… - Applied Sciences, 2022 - mdpi.com
Computer-aided diagnosis (CAD) systems can help radiologists in numerous medical tasks
including classification and staging of the various diseases. The 3D tomosynthesis imaging …

A performance comparison between shallow and deeper neural networks supervised classification of tomosynthesis breast lesions images

V Bevilacqua, A Brunetti, A Guerriero, GF Trotta… - Cognitive Systems …, 2019 - Elsevier
Abstract Computer Aided Decision (CAD) systems, based on 3D tomosynthesis imaging,
could support radiologists in classifying different kinds of breast lesions and then improve …

Intelligent Computer-aided model for efficient diagnosis of digital breast tomosynthesis 3D imaging using Deep Learning

AMA El-Shazli, SM Youssef, AH Soliman - Applied Sciences, 2022 - mdpi.com
Digital breast tomosynthesis (DBT) is a highly promising 3D imaging modality for breast
diagnosis. Tissue overlapping is a challenge with traditional 2D mammograms; however …

Transformer-based deep neural network for breast cancer classification on digital breast tomosynthesis images

W Lee, H Lee, H Lee, EK Park, H Nam… - Radiology: Artificial …, 2023 - pubs.rsna.org
Purpose To develop an efficient deep neural network model that incorporates context from
neighboring image sections to detect breast cancer on digital breast tomosynthesis (DBT) …

A method for the automated classification of benign and malignant masses on digital breast tomosynthesis images using machine learning and radiomic features

A Sakai, Y Onishi, M Matsui, H Adachi… - … Physics and Technology, 2020 - Springer
In digital mammography, which is used for the early detection of breast tumors, oversight
may occur due to overlap between normal tissues and lesions. However, since digital breast …

A deep learning classifier for digital breast tomosynthesis

R Ricciardi, G Mettivier, M Staffa, A Sarno, G Acampora… - Physica Medica, 2021 - Elsevier
Purpose To develop a computerized detection system for the automatic classification of the
presence/absence of mass lesions in digital breast tomosynthesis (DBT) annotated exams …

Transfer learning from convolutional neural networks for computer-aided diagnosis: a comparison of digital breast tomosynthesis and full-field digital mammography

K Mendel, H Li, D Sheth, M Giger - Academic radiology, 2019 - Elsevier
Rationale and Objectives With the growing adoption of digital breast tomosynthesis (DBT) in
breast cancer screening, we compare the performance of deep learning computer-aided …

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

RK Samala, HP Chan, LM Hadjiiski… - Medical Imaging …, 2016 - spiedigitallibrary.org
A deep learning convolution neural network (DLCNN) was designed to differentiate
microcalcification candidates detected during the prescreening stage as true calcifications or …

2d convolutional neural networks for 3d digital breast tomosynthesis classification

Y Zhang, X Wang, H Blanton, G Liang… - 2019 IEEE …, 2019 - ieeexplore.ieee.org
Automated methods for breast cancer detection have focused on 2D mammography and
have largely ignored 3D digital breast tomosynthesis (DBT), which is frequently used in …

Mass detection and segmentation in digital breast tomosynthesis using 3D-mask region-based convolutional neural network: a comparative analysis

M Fan, H Zheng, S Zheng, C You, Y Gu… - Frontiers in molecular …, 2020 - frontiersin.org
Digital breast tomosynthesis (DBT) is an emerging breast cancer screening and diagnostic
modality that uses quasi-three-dimensional breast images to provide detailed assessments …