AIMC Topic: Mammography
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Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification.
Scientific reports
Apr 9, 2025
Breast cancer detection remains one of the most challenging problems in medical imaging. We propose a novel hybrid model that integrates Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (Bi-LSTM) networks, and EfficientNet-B...
A Multitask CNN for Near-Infrared Probe: Enhanced Real-Time Breast Cancer Imaging.
Sensors (Basel, Switzerland)
Apr 8, 2025
The early detection of breast cancer, particularly in dense breast tissues, faces significant challenges with traditional imaging techniques such as mammography. This study utilizes a Near-infrared Scan (NIRscan) probe and an advanced convolutional n...
Using artificial intelligence system for assisting the classification of breast ultrasound glandular tissue components in dense breast tissue.
Scientific reports
Apr 6, 2025
To investigate the potential of employing artificial intelligence (AI) -driven breast ultrasound analysis models for the classification of glandular tissue components (GTC) in dense breast tissue. A total of 1,848 healthy women with mammograms classi...
Deep learning prediction of mammographic breast density using screening data.
Scientific reports
Apr 4, 2025
This study investigated a series of deep learning (DL) models for the objective assessment of four categories of mammographic breast density (e.g., fatty, scattered, heterogeneously dense, and extremely dense). A retrospective analysis was conducted ...
VBVT-Net: VOI-Based VVBP-Tensor Network for High-Attenuation Artifact Suppression in Digital Breast Tomosynthesis Imaging.
IEEE transactions on medical imaging
Apr 3, 2025
High-attenuation (HA) artifacts may lead to obscured subtle lesions and lesion over-estimation in digital breast tomosynthesis (DBT) imaging. High-attenuation artifact suppression (HAAS) is vital for widespread DBT applications in clinic. The convent...
Global disparities in artificial intelligence-based mammogram interpretation for breast cancer: A scientometric analysis of representation, trends, and equity.
European journal of cancer (Oxford, England : 1990)
Apr 1, 2025
BACKGROUND: Breast cancer (BC) is the most frequently diagnosed cancer and the leading cause of cancer death among women worldwide. Artificial intelligence (AI) shows promise for improving mammogram interpretation, especially in resource-limited sett...
Dual-Modality Virtual Biopsy System Integrating MRI and MG for Noninvasive Predicting HER2 Status in Breast Cancer.
Academic radiology
Mar 10, 2025
RATIONALE AND OBJECTIVES: Accurate determination of human epidermal growth factor receptor 2 (HER2) expression is critical for guiding targeted therapy in breast cancer. This study aimed to develop and validate a deep learning (DL)-based decision-mak...
Economics of AI and human task sharing for decision making in screening mammography.
Nature communications
Mar 7, 2025
The rising global incidence of breast cancer and the persistent shortage of specialized radiologists have heightened the demand for innovative solutions in mammography screening. Artificial intelligence (AI) has emerged as a promising tool to bridge ...
Artificial intelligence for breast cancer screening in mammography (AI-STREAM): preliminary analysis of a prospective multicenter cohort study.
Nature communications
Mar 6, 2025
Artificial intelligence (AI) improves the accuracy of mammography screening, but prospective evidence, particularly in a single-read setting, remains limited. This study compares the diagnostic accuracy of breast radiologists with and without AI-base...
Integrating radiomics into predictive models for low nuclear grade DCIS using machine learning.
Scientific reports
Mar 3, 2025
Predicting low nuclear grade DCIS before surgery can improve treatment choices and patient care, thereby reducing unnecessary treatment. Due to the high heterogeneity of DCIS and the limitations of biopsies in fully characterizing tumors, current dia...
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