Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment

A Gastounioti, EF Conant, D Kontos - Breast cancer research, 2016 - Springer
Background The assessment of a woman's risk for develo** breast cancer has become
increasingly important for establishing personalized screening recommendations and …

A review of biomechanically informed breast image registration

JH Hipwell, V Vavourakis, L Han… - Physics in Medicine …, 2016 - iopscience.iop.org
Breast radiology encompasses the full range of imaging modalities from routine imaging via
x-ray mammography, magnetic resonance imaging and ultrasound (both two-and three …

Saliency based mass detection from screening mammograms

P Agrawal, M Vatsa, R Singh - Signal processing, 2014 - Elsevier
Screening mammography has been successful in early detection of breast cancer, which
has been one of the leading causes of death for women worldwide. Among commonly …

A two-step deep learning method for 3DCT-2DUS kidney registration during breathing

Y Chi, Y Xu, H Liu, X Wu, Z Liu, J Mao, G Xu… - Scientific reports, 2023 - nature.com
This work proposed KidneyRegNet, a novel deep registration pipeline for 3D CT and 2D U/S
kidney scans of free breathing, which comprises a feature network, and a 3D–2D CNN …

Supervised two-dimensional functional principal component analysis with time-to-event outcomes and mammogram imaging data

S Jiang, J Cao, B Rosner, GA Colditz - Biometrics, 2023 - academic.oup.com
Screening mammography aims to identify breast cancer early and secondarily measures
breast density to classify women at higher or lower than average risk for future breast cancer …

Temporal mammogram image registration using optimized curvilinear coordinates

M Abdel-Nasser, A Moreno, D Puig - Computer methods and programs in …, 2016 - Elsevier
Registration of mammograms plays an important role in breast cancer computer-aided
diagnosis systems. Radiologists usually compare mammogram images in order to detect …

Optimized radial basis neural network for classification of breast cancer images

GM Rajathi - Current Medical Imaging, 2021 - ingentaconnect.com
Background: Breast cancer is a curable disease if diagnosed at an early stage. The chances
of having breast cancer are the lowest in married women after the breast-feeding phase …

Fusion of deep learning and image processing techniques for breast cancer diagnosis

V Ajantha Devi, A Nayyar - Deep learning for cancer diagnosis, 2021 - Springer
Deep learning has the capacity to gain great accuracy of diagnosing of numerous types of
cancers, along with lung, cervical, colon, and breast cancer. It builds an efficient algorithm …

A bilateral analysis scheme for false positive reduction in mammogram mass detection

Y Li, H Chen, Y Yang, L Cheng, L Cao - Computers in biology and …, 2015 - Elsevier
In this paper, a bilateral image analysis scheme is developed for the purpose of reducing
false positives (FPs) in the detection of masses in dense mammograms. It consists of two …

Mammographic texture versus conventional Cumulus measure of density in breast cancer risk prediction: a literature review

Z Ye, TL Nguyen, GS Dite, RJ MacInnis… - … Biomarkers & Prevention, 2024 - AACR
Mammographic textures show promise as breast cancer risk predictors, distinct from
mammographic density. Yet, there is a lack of comprehensive evidence to determine the …