AIMC Topic: Mammography
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Segmentation for mammography classification utilizing deep convolutional neural network.
BMC medical imaging
Dec 18, 2024
BACKGROUND: Mammography for the diagnosis of early breast cancer (BC) relies heavily on the identification of breast masses. However, in the early stages, it might be challenging to ascertain whether a breast mass is benign or malignant. Consequently...
Predicting axillary lymph node metastasis in breast cancer using a multimodal radiomics and deep learning model.
Frontiers in immunology
Dec 13, 2024
OBJECTIVE: To explore the value of combined radiomics and deep learning models using different machine learning algorithms based on mammography (MG) and magnetic resonance imaging (MRI) for predicting axillary lymph node metastasis (ALNM) in breast c...
Artificial intelligence improves mammography-based breast cancer risk prediction.
Trends in cancer
Dec 12, 2024
Artificial intelligence (AI) is enabling us to delve deeply into the information inherent in a mammogram and identify novel features associated with high risk of a future breast cancer diagnosis. Here, we discuss how AI is improving mammographic dens...
CNN-Based Cross-Modality Fusion for Enhanced Breast Cancer Detection Using Mammography and Ultrasound.
Tomography (Ann Arbor, Mich.)
Dec 12, 2024
Breast cancer is a leading cause of mortality among women in Taiwan and globally. Non-invasive imaging methods, such as mammography and ultrasound, are critical for early detection, yet standalone modalities have limitations in regard to their diagn...
Breast cancer detection and classification with digital breast tomosynthesis: a two-stage deep learning approach.
Diagnostic and interventional radiology (Ankara, Turkey)
Dec 9, 2024
PURPOSE: The purpose of this study was to propose a new computer-assisted two-staged diagnosis system that combines a modified deep learning (DL) architecture (VGG19) for the classification of digital breast tomosynthesis (DBT) images with the detect...
Domain generalization for mammographic image analysis with contrastive learning.
Computers in biology and medicine
Dec 9, 2024
The deep learning technique has been shown to be effectively addressed several image analysis tasks in the computer-aided diagnosis scheme for mammography. The training of an efficacious deep learning model requires large data with diverse styles and...
Volumetric Breast Density Estimation From Three-Dimensional Reconstructed Digital Breast Tomosynthesis Images Using Deep Learning.
JCO clinical cancer informatics
Dec 9, 2024
PURPOSE: Breast density is a widely established independent breast cancer risk factor. With the increasing utilization of digital breast tomosynthesis (DBT) in breast cancer screening, there is an opportunity to estimate volumetric breast density (VB...
Deep Learning for Contrast Enhanced Mammography - A Systematic Review.
Academic radiology
Dec 5, 2024
BACKGROUND/AIM: Contrast-enhanced mammography (CEM) is a relatively novel imaging technique that enables both anatomical and functional breast imaging, with improved diagnostic performance compared to standard 2D mammography. The aim of this study is...
A multimodal machine learning model for the stratification of breast cancer risk.
Nature biomedical engineering
Dec 4, 2024
Machine learning models for the diagnosis of breast cancer can facilitate the prediction of cancer risk and subsequent patient management among other clinical tasks. For the models to impact clinical practice, they ought to follow standard workflows,...
The Transformative Power of Digital Breast Tomosynthesis and Artificial Intelligence in Breast Cancer Diagnosis.
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Dec 3, 2024
The integration of Digital Breast Tomosynthesis (DBT) and Artificial Intelligence (AI) represents a significant advance in breast cancer screening. This combination aims to address several challenges inherent in traditional screening while promising ...
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