AIMC Topic: Mammography

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K-Means Clustering and Classification of Breast Cancer Images Using Histogram of Oriented Gradients Features and Convolutional Neural Network Models: Diagnostic Image Analysis Study.

JMIR formative research Jul 29, 2025
BACKGROUND: Breast cancer has proven to be the most common type of cancer among females around the world. However, mortality rates can be reduced if it is diagnosed at the initial stages. Interpretation made by an expert is required by conventional d...
Cluster Analysis Mammography Image Interpretation, Computer-Assisted Breast Neoplasms Convolutional Neural Networks Image Processing, Computer-Assisted Humans Neural Networks, Computer Female Reproducibility of Results
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Development and validation of an improved volumetric breast density estimation model using the ResNet technique.

Biomedical physics & engineering express Jul 23, 2025
. Temporal changes in volumetric breast density (VBD) may serve as prognostic biomarkers for predicting the risk of future breast cancer development. However, accurately measuring VBD from archived x-ray mammograms remains challenging. In a previous ...
Breast Breast Density Deep Learning Image Processing, Computer-Assisted Mammography Algorithms Female Reproducibility of Results Aged Adult Breast Neoplasms Middle Aged Humans Machine Learning
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Enhancing breast cancer classification using a deep sparse wavelet autoencoder approach.

Scientific reports Jul 19, 2025
As digital imaging technology advances, accurate classification of 2D breast cancer images becomes increasingly crucial for early detection and staging. This paper introduces a novel classification approach that integrates deep learning, sparse codin...
Image Interpretation, Computer-Assisted Wavelet Analysis Autoencoder Breast Neoplasms Humans Deep Learning Algorithms Image Processing, Computer-Assisted Female Mammography
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Robust Bi-CBMSegNet framework for advancing breast mass segmentation in mammography with a dual module encoder-decoder approach.

Scientific reports Jul 8, 2025
Breast cancer is a prevalent disease affecting millions of women worldwide, and early screening can significantly reduce mortality rates. Mammograms are widely used for screening, but manual readings can lead to misdiagnosis. Computer-assisted diagno...
Humans Algorithms Early Detection of Cancer Mammography Radiographic Image Interpretation, Computer-Assisted Breast Female Breast Neoplasms Image Processing, Computer-Assisted
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Multi-modality radiomics diagnosis of breast cancer based on MRI, ultrasound and mammography.

BMC medical imaging Jul 4, 2025
OBJECTIVE: To develop a multi-modality machine learning-based radiomics model utilizing Magnetic Resonance Imaging (MRI), Ultrasound (US), and Mammography (MMG) for the differentiation of benign and malignant breast nodules.
Diagnosis, Differential Ultrasonography, Mammary Magnetic Resonance Imaging Mammography Female Multimodal Imaging Aged Adult Breast Neoplasms Humans Radiomics Machine Learning Middle Aged Retrospective Studies ROC Curve
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Contrast-enhanced mammography-based interpretable machine learning model for the prediction of the molecular subtype breast cancers.

BMC medical imaging Jul 1, 2025
OBJECTIVE: This study aims to establish a machine learning prediction model to explore the correlation between contrast-enhanced mammography (CEM) imaging features and molecular subtypes of mass-type breast cancer.
Mammography Radiographic Image Interpretation, Computer-Assisted Breast Neoplasms Middle Aged Triple Negative Breast Neoplasms Contrast Media Female Aged Adult Humans Machine Learning Retrospective Studies
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Innovative deep learning classifiers for breast cancer detection through hybrid feature extraction techniques.

Scientific reports Jul 1, 2025
Breast cancer remains a major cause of mortality among women, where early and accurate detection is critical to improving survival rates. This study presents a hybrid classification approach for mammogram analysis by combining handcrafted statistical...
Humans Neural Networks, Computer Algorithms Female Breast Neoplasms Mammography Deep Learning Image Processing, Computer-Assisted Early Detection of Cancer
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Multi-site validation of an interpretable model to analyze breast masses.

PloS one Jun 26, 2025
An external validation of IAIA-BL-a deep-learning based, inherently interpretable breast lesion malignancy prediction model-was performed on two patient populations: 207 women ages 31 to 96, (425 mammograms) from iCAD, and 58 women (104 mammograms) f...
Aged Aged, 80 and over Adult Breast Neoplasms Breast Middle Aged Deep Learning Humans Mammography Female
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Comparative analysis of semantic-segmentation models for screen film mammograms.

Computers in biology and medicine Jun 5, 2025
Accurate segmentation of mammographic mass is very important as shape characteristics of these masses play a significant role for radiologist to diagnose benign and malignant cases. Recently, various deep learning segmentation algorithms have become ...
Humans Deep Learning Mammography Radiographic Image Interpretation, Computer-Assisted Semantics Algorithms Databases, Factual Female Breast Neoplasms
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Preoperative DBT-based radiomics for predicting axillary lymph node metastasis in breast cancer: a multi-center study.

BMC medical imaging May 19, 2025
BACKGROUND: In the prognosis of breast cancer, the status of axillary lymph nodes (ALN) is critically important. While traditional axillary lymph node dissection (ALND) provides comprehensive information, it is associated with high risks. Sentinel ly...
Middle Aged Lymph Nodes Aged Sentinel Lymph Node Biopsy Lymphatic Metastasis Axilla Humans Adult Mammography Retrospective Studies Breast Neoplasms Feasibility Studies Female Radiomics Preoperative Care
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