AIMC Topic: Breast
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Predicting Reduction Mammaplasty Total Resection Weight With Machine Learning.
Annals of plastic surgery
Jun 4, 2024
BACKGROUND: Machine learning (ML) is a form of artificial intelligence that has been used to create better predictive models in medicine. Using ML algorithms, we sought to create a predictive model for breast resection weight based on anthropometric ...
Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images.
Breast cancer research : BCR
Jun 3, 2024
BACKGROUND: Nottingham histological grade (NHG) is a well established prognostic factor in breast cancer histopathology but has a high inter-assessor variability with many tumours being classified as intermediate grade, NHG2. Here, we evaluate if Dee...
Mammographic density assessment: comparison of radiologists, automated volumetric measurement, and artificial intelligence-based computer-assisted diagnosis.
Acta radiologica (Stockholm, Sweden : 1987)
Jun 2, 2024
BACKGROUND: Artificial intelligence-based computer-assisted diagnosis (AI-CAD) is increasingly used for mammographic exams, and its role in mammographic density assessment should be evaluated.
DAU-Net: Dual attention-aided U-Net for segmenting tumor in breast ultrasound images.
PloS one
May 31, 2024
Breast cancer remains a critical global concern, underscoring the urgent need for early detection and accurate diagnosis to improve survival rates among women. Recent developments in deep learning have shown promising potential for computer-aided det...
MRI-based Deep Learning Models for Preoperative Breast Volume and Density Assessment Assisting Breast Reconstruction.
Aesthetic plastic surgery
May 28, 2024
BACKGROUND: The volume of the implant is the most critical element of breast reconstruction, so it is necessary to accurately assess the preoperative volume of the healthy and affected breasts and select the appropriate implant for placement. Accurat...
Deep learning of mammogram images to reduce unnecessary breast biopsies: a preliminary study.
Breast cancer research : BCR
May 24, 2024
BACKGROUND: Patients with a Breast Imaging Reporting and Data System (BI-RADS) 4 mammogram are currently recommended for biopsy. However, 70-80% of the biopsies are negative/benign. In this study, we developed a deep learning classification algorithm...
Deep-learning model for background parenchymal enhancement classification in contrast-enhanced mammography.
Physics in medicine and biology
May 20, 2024
Breast background parenchymal enhancement (BPE) is correlated with the risk of breast cancer. BPE level is currently assessed by radiologists in contrast-enhanced mammography (CEM) using 4 classes: minimal, mild, moderate and marked, as described in(...
MFMSNet: A Multi-frequency and Multi-scale Interactive CNN-Transformer Hybrid Network for breast ultrasound image segmentation.
Computers in biology and medicine
May 15, 2024
Breast tumor segmentation in ultrasound images is fundamental for quantitative analysis and plays a crucial role in the diagnosis and treatment of breast cancer. Recently, existing methods have mainly focused on spatial domain implementations, with l...
Breast density prediction from low and standard dose mammograms using deep learning: effect of image resolution and model training approach on prediction quality.
Biomedical physics & engineering express
May 15, 2024
. To improve breast cancer risk prediction for young women, we have developed deep learning methods to estimate mammographic density from low dose mammograms taken at approximately 1/10th of the usual dose. We investigate the quality and reliability ...
Preoperative Differentiation of HER2-Zero and HER2-Low from HER2-Positive Invasive Ductal Breast Cancers Using BI-RADS MRI Features and Machine Learning Modeling.
Journal of magnetic resonance imaging : JMRI
May 10, 2024
BACKGROUND: Accurate determination of human epidermal growth factor receptor 2 (HER2) is important for choosing optimal HER2 targeting treatment strategies. HER2-low is currently considered HER2-negative, but patients may be eligible to receive new a...
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