AIMC Topic: Breast Neoplasms

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Machine learning prediction of breast cancer local recurrence localization, and distant metastasis after local recurrences.

Scientific reports Feb 10, 2025
Local recurrences (LR) can occur within residual breast tissue, chest wall, skin, or newly formed scar tissue. Artificial intelligence (AI) technologies can extract a wide range of tumor features from large datasets helping in oncological decision-ma...
ROC Curve Neoplasm Recurrence, Local Neoplasm Metastasis Adult Breast Neoplasms Middle Aged Humans Machine Learning Prognosis Female Aged
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A Deep Reinforcement Learning-Based Feature Selection Method for Invasive Disease Event Prediction Using Imbalanced Follow-Up Data.

IEEE journal of biomedical and health informatics Feb 10, 2025
The machine learning-based model is a promising paradigm for predicting invasive disease events (iDEs) in breast cancer. Feature selection (FS) is an essential preprocessing technique employed to identify the pertinent features for the prediction mod...
Prognosis Female Humans Breast Neoplasms Neoplasm Invasiveness Datasets as Topic Reinforcement Machine Learning
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An assessment of breast cancer HER2, ER, and PR expressions based on mammography using deep learning with convolutional neural networks.

Scientific reports Feb 9, 2025
Mammography is the recommended imaging modality for breast cancer screening. Expressions of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR) are critical to the development of therapeutic strateg...
Humans Neural Networks, Computer Female Receptor, ErbB-2 Adult Convolutional Neural Networks Biomarkers, Tumor Receptors, Estrogen ROC Curve Middle Aged Receptors, Progesterone Breast Neoplasms Mammography Deep Learning
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Next-generation sequencing based deep learning model for prediction of HER2 status and response to HER2-targeted neoadjuvant chemotherapy.

Journal of cancer research and clinical oncology Feb 9, 2025
INTRODUCTION: For patients with breast cancer, the amplification of Human Epidermal Growth Factor 2 (HER2) is closely related to their prognosis and treatment decisions. This study aimed to further improve the accuracy and efficiency of HER2 amplific...
High-Throughput Nucleotide Sequencing Receptor, ErbB-2 Biomarkers, Tumor Middle Aged Deep Learning Antineoplastic Combined Chemotherapy Protocols Neoadjuvant Therapy Mutation Female Adult Breast Neoplasms Humans Prognosis
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QMaxViT-Unet+: A query-based MaxViT-Unet with edge enhancement for scribble-supervised segmentation of medical images.

Computers in biology and medicine Feb 6, 2025
The deployment of advanced deep learning models for medical image segmentation is often constrained by the requirement for extensively annotated datasets. Weakly-supervised learning, which allows less precise labels, has become a promising solution t...
Humans Algorithms Databases, Factual Supervised Machine Learning Breast Neoplasms Deep Learning Image Processing, Computer-Assisted
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[Artificial intelligence in breast imaging : Hopes and challenges].

Radiologie (Heidelberg, Germany) Feb 6, 2025
CLINICAL/METHODICAL ISSUE: Artificial intelligence (AI) is being increasingly integrated into clinical practice. However, the specific benefits are still unclear to many users.
Humans Artificial Intelligence Mammography Female Breast Neoplasms
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Machine learning identifies the association between second primary malignancies and postoperative radiotherapy in young-onset breast cancer patients.

PloS one Feb 6, 2025
BACKGROUND: A second primary malignant tumor is one of the most important factors affecting the long-term survival of young women with breast cancer (YWBC). As one of the main treatments for breast cancer YWBC patients, postoperative radiotherapy (PO...
Neoplasms, Second Primary Adult Female Breast Neoplasms Humans Incidence Risk Factors Middle Aged Machine Learning Young Adult SEER Program Radiotherapy, Adjuvant
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Deep learning-based breast cancer diagnosis in breast MRI: systematic review and meta-analysis.

European radiology Feb 5, 2025
OBJECTIVES: The aim of this work is to evaluate the performance of deep learning (DL) models for breast cancer diagnosis with MRI.
Humans Deep Learning Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Female Sensitivity and Specificity Breast Neoplasms
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Class-aware multi-level attention learning for semi-supervised breast cancer diagnosis under imbalanced label distribution.

Medical & biological engineering & computing Feb 5, 2025
Breast cancer affects a significant number of patients worldwide, and early diagnosis is critical for improving cure rates and prognosis. Deep learning-based breast cancer classification algorithms have substantially alleviated the burden on medical ...
Deep Learning Supervised Machine Learning Algorithms Humans Female Breast Neoplasms
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Chan-Vese aided fuzzy C-means approach for whole breast and fibroglandular tissue segmentation: Preliminary application to real-world breast MRI.

Medical physics Feb 5, 2025
BACKGROUND: Magnetic resonance imaging (MRI) is a highly sensitive modality for diagnosing breast cancer, providing an expanding range of clinical usages that are crucial for the care of women at elevated risk of breast cancer development. Segmentati...
Breast Neoplasms Image Processing, Computer-Assisted Humans Magnetic Resonance Imaging Fuzzy Logic Female Breast
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