New frontiers: an update on computer-aided diagnosis for breast imaging in the age of artificial intelligence
OBJECTIVE. The purpose of this article is to compare traditional versus machine learning–
based computer-aided detection (CAD) platforms in breast imaging with a focus on …
based computer-aided detection (CAD) platforms in breast imaging with a focus on …
A review of computer aided detection in mammography
J Katzen, K Dodelzon - Clinical imaging, 2018 - Elsevier
Breast screening with mammography is widely recognized as the most effective method of
detecting early breast cancer and has consistently demonstrated a 20–40% decrease in …
detecting early breast cancer and has consistently demonstrated a 20–40% decrease in …
Breast cancer diagnosis in digital breast tomosynthesis: effects of training sample size on multi-stage transfer learning using deep neural nets
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 …
of malignant and benign masses in digital breast tomosynthesis (DBT) using a multi-stage …
Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography
Purpose: Develop a computer‐aided detection (CAD) system for masses in digital breast
tomosynthesis (DBT) volume using a deep convolutional neural network (DCNN) with …
tomosynthesis (DBT) volume using a deep convolutional neural network (DCNN) with …
Digital breast tomosynthesis with synthesized two-dimensional images versus full-field digital mammography for population screening: outcomes from the Verona …
F Caumo, M Zorzi, S Brunelli, G Romanucci, R Rella… - Radiology, 2018 - pubs.rsna.org
Purpose To examine the outcomes of a breast cancer screening program based on digital
breast tomosynthesis (DBT) plus synthesized two-dimensional (2D) mammography …
breast tomosynthesis (DBT) plus synthesized two-dimensional (2D) mammography …
Transfer learning from convolutional neural networks for computer-aided diagnosis: a comparison of digital breast tomosynthesis and full-field digital mammography
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 …
breast cancer screening, we compare the performance of deep learning computer-aided …
Federated and transfer learning for cancer detection based on image analysis
This review highlights the efficacy of combining federated learning (FL) and transfer learning
(TL) for cancer detection via image analysis. By integrating these techniques, research has …
(TL) for cancer detection via image analysis. By integrating these techniques, research has …
YOLOv4-based CNN model versus nested contours algorithm in the suspicious lesion detection on the mammography image: A direct comparison in the real clinical …
A Kolchev, D Pasynkov, I Egoshin, I Kliouchkin… - Journal of …, 2022 - mdpi.com
Background: We directly compared the mammography image processing results obtained
with the help of the YOLOv4 convolutional neural network (CNN) model versus those …
with the help of the YOLOv4 convolutional neural network (CNN) model versus those …
[HTML][HTML] Mammography classification with multi-view deep learning techniques: Investigating graph and transformer-based architectures
The potential and promise of deep learning systems to provide an independent assessment
and relieve radiologists' burden in screening mammography have been recognized in …
and relieve radiologists' burden in screening mammography have been recognized in …
Synthesized mammography: clinical evidence, appearance, and implementation
MA Durand - Diagnostics, 2018 - mdpi.com
Digital breast tomosynthesis (DBT) has improved conventional mammography by increasing
cancer detection while reducing recall rates. However, these benefits come at the cost of …
cancer detection while reducing recall rates. However, these benefits come at the cost of …