AIMC Topic: Breast Neoplasms
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RAE-Net: a multi-modal neural network based on feature fusion and evidential deep learning algorithm in predicting breast cancer subtypes on DCE-MRI.
Biomedical physics & engineering express
Feb 25, 2025
Accurate identification of molecular subtypes in breast cancer is critical for personalized treatment. This study introduces a novel neural network model, RAE-Net, based on Multimodal Feature Fusion (MFF) and the Evidential Deep Learning Algorithm (E...
An enhanced denoising system for mammogram images using deep transformer model with fusion of local and global features.
Scientific reports
Feb 24, 2025
Image denoising is a critical problem in low-level computer vision, where the aim is to reconstruct a clean, noise-free image from a noisy input, such as a mammogram image. In recent years, deep learning, particularly convolutional neural networks (C...
From text to insight: A natural language processing-based analysis of burst and research trends in HER2-low breast cancer patients.
Ageing research reviews
Feb 22, 2025
With the intensification of population aging, the proportion of elderly breast cancer patients is continuously increasing, among which those with low HER2 expression account for approximately 45 %-55 % of all cases within traditional HER2-negative br...
A multi-task self-supervised approach for mass detection in automated breast ultrasound using double attention recurrent residual U-Net.
Computers in biology and medicine
Feb 20, 2025
Breast cancer is the most common and lethal cancer among women worldwide. Early detection using medical imaging technologies can significantly improve treatment outcomes. Automated breast ultrasound, known as ABUS, offers more advantages compared to ...
Assessment of breast composition in MRI using artificial intelligence - A systematic review.
Radiography (London, England : 1995)
Feb 20, 2025
INTRODUCTION: Magnetic Resonance Imaging (MRI) performs a critical role in breast cancer diagnosis, especially for high-risk populations e.g. family history. MRI could take advantage of the implementation of artificial intelligence (AI). AI assessmen...
Multimodal deep learning fusion of ultrafast-DCE MRI and clinical information for breast lesion classification.
Computers in biology and medicine
Feb 19, 2025
BACKGROUND: Breast cancer is the most common cancer worldwide, and magnetic resonance imaging (MRI) constitutes a very sensitive technique for invasive cancer detection. When reviewing breast MRI examination, clinical radiologists rely on multimodal ...
Enhancing automated right-sided early-stage breast cancer treatments via deep learning model adaptation without additional training.
Medical physics
Feb 18, 2025
BACKGROUND: Input data curation and model training are essential, but time-consuming steps in building a deep-learning (DL) auto-planning model, ensuring high-quality data and optimized performance. Ideally, one would prefer a DL model that exhibits ...
Impact of pectoral muscle removal on deep-learning-based breast cancer risk prediction.
Physics in medicine and biology
Feb 18, 2025
State-of-the-art breast cancer risk (BCR) prediction models have been originally trained on mammograms with pectoral muscle (PM) included. This study investigated whether excluding PM during training/fine-tuning improves the model's BCR discriminatio...
Leveraging natural language processing for efficient information extraction from breast cancer pathology reports: Single-institution study.
PloS one
Feb 18, 2025
BACKGROUND: Pathology reports provide important information for accurate diagnosis of cancer and optimal treatment decision making. In particular, breast cancer has known to be the most common cancer in women worldwide.
Diagnosis of Benign and Malignant Newly Developed Nodules on the Surgical Side After Breast Cancer Surgery Based on Machine Learning.
The breast journal
Feb 17, 2025
To enhance the diagnostic accuracy of new nodules on the surgical side after breast cancer surgery using machine learning techniques and to explore the role of multifeature fusion. Data from 137 breast cancer postoperative patients with new nodules...
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