AIMC Topic: Breast

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Development and validation of an improved volumetric breast density estimation model using the ResNet technique.

Biomedical physics & engineering express
. 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 ...

Robust Bi-CBMSegNet framework for advancing breast mass segmentation in mammography with a dual module encoder-decoder approach.

Scientific reports
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...

Improving YOLO-based breast mass detection with transfer learning pretraining on the OPTIMAM Mammography Image Database.

Computers in biology and medicine
BACKGROUND AND OBJECTIVES: Early detection of breast cancer through mammography significantly improves survival rates. However, high false positive and false negative rates remain a challenge. Deep learning-based computer-aided diagnosis systems can ...

Multi-site validation of an interpretable model to analyze breast masses.

PloS one
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...

Multi-parameter MRI deep learning model for lymphovascular invasion assessment in invasive breast ductal carcinoma: A multicenter, retrospective study.

Clinical radiology
AIMS: To investigate the value of multi-parametric magnetic resonance imaging (MRI)-based deep learning (DL) in predicting the Lymphovascular Invasion (LVI) status of invasive breast ductal cancer (IBDC).

Detection of breast cancer using fractional discrete sinc transform based on empirical Fourier decomposition.

Bio-medical materials and engineering
Breast cancer is the most common cause of death among women worldwide. Early detection of breast cancer is important; for saving patients' lives. Ultrasound and mammography are the most common noninvasive methods for detecting breast cancer. Computer...

Quantification of Breast Arterial Calcification in Mammograms Using a UNet-Based Deep Learning for Detecting Cardiovascular Disease.

Academic radiology
BACKGROUND: Breast arterial calcification (BAC) is increasingly recognized as a significant indicator of cardiovascular risk, necessitating improvements in detection and quantification methods through mammographic screening.

Exploratory multi-cohort, multi-reader study on the clinical utility of a deep learning model for transforming cryosectioned to formalin-fixed, paraffin-embedded (FFPE) images in breast lesion diagnosis.

Breast cancer research : BCR
BACKGROUND: Cryosectioned tissues often exhibit artifacts that compromise pathologists' diagnostic accuracy during intraoperative assessments. These inconsistencies, compounded by variations in frozen section (FS) production across laboratories, high...

Application Value of Deep Learning-Based AI Model in the Classification of Breast Nodules.

British journal of hospital medicine (London, England : 2005)
Breast nodules are highly prevalent among women, and ultrasound is a widely used screening tool. However, single ultrasound examinations often result in high false-positive rates, leading to unnecessary biopsies. Artificial intelligence (AI) has dem...