AI-based strategies to reduce workload in breast cancer screening with mammography and tomosynthesis: a retrospective evaluation

JL Raya-Povedano, S Romero-Martín, E Elías-Cabot… - Radiology, 2021 - pubs.rsna.org
Background The workflow of breast cancer screening programs could be improved given the
high workload and the high number of false-positive and false-negative assessments …

Standalone AI for breast cancer detection at screening digital mammography and digital breast tomosynthesis: a systematic review and meta-analysis

JH Yoon, F Strand, PAT Baltzer, EF Conant, FJ Gilbert… - Radiology, 2023 - pubs.rsna.org
Background There is considerable interest in the potential use of artificial intelligence (AI)
systems in mammographic screening. However, it is essential to critically evaluate the …

Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification

X Li, G Qin, Q He, L Sun, H Zeng, Z He, W Chen… - European …, 2020 - Springer
Objective To evaluate the impact of utilizing digital breast tomosynthesis (DBT) or/and full-
field digital mammography (FFDM), and different transfer learning strategies on deep …

Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography

RK Samala, HP Chan, L Hadjiiski, MA Helvie… - Medical …, 2016 - Wiley Online Library
Purpose: Develop a computer‐aided detection (CAD) system for masses in digital breast
tomosynthesis (DBT) volume using a deep convolutional neural network (DCNN) with …

Digital breast tomosynthesis for breast cancer detection: a diagnostic test accuracy systematic review and meta-analysis

M Alabousi, N Zha, JP Salameh, L Samoilov… - European …, 2020 - Springer
Objectives No consensus exists on digital breast tomosynthesis (DBT) utilization for breast
cancer detection. We performed a diagnostic test accuracy systematic review and meta …

Breast cancer diagnosis in digital breast tomosynthesis: effects of training sample size on multi-stage transfer learning using deep neural nets

RK Samala, HP Chan, L Hadjiiski… - IEEE transactions on …, 2018 - ieeexplore.ieee.org
In this paper, we developed a deep convolutional neural network (CNN) for the classification
of malignant and benign masses in digital breast tomosynthesis (DBT) using a multi-stage …

A risk model for digital breast tomosynthesis to predict breast cancer and guide clinical care

M Eriksson, S Destounis, K Czene, A Zeiberg… - Science Translational …, 2022 - science.org
Screening with digital breast tomosynthesis (DBT) improves breast cancer detection and
reduces false positives. However, currently, no breast cancer risk model takes advantage of …

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 …

Increased cancer detection rate and variations in the recall rate resulting from implementation of 3D digital breast tomosynthesis into a population-based screening …

RE Sharpe Jr, S Venkataraman, J Phillips, V Dialani… - Radiology, 2016 - pubs.rsna.org
Purpose To compare the recall and cancer detection rates (CDRs) at screening with digital
breast tomosynthesis (DBT) with those at screening with two-dimensional (2D) …

Computer-aided detection of mass in digital breast tomosynthesis using a faster region-based convolutional neural network

M Fan, Y Li, S Zheng, W Peng, W Tang, L Li - Methods, 2019 - Elsevier
Digital breast tomosynthesis (DBT) is a newly developed three-dimensional tomographic
imaging modality in the field of breast cancer screening designed to alleviate the limitations …