AIMC Topic: Breast Neoplasms

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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 Breast Neoplasms Humans 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...
Convolutional Neural Networks ROC Curve Mammography Receptor, ErbB-2 Receptors, Estrogen Humans Receptors, Progesterone Neural Networks, Computer Biomarkers, Tumor Female Middle Aged Deep Learning Adult Breast Neoplasms
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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...
Receptor, ErbB-2 Middle Aged Adult Deep Learning Breast Neoplasms Humans Antineoplastic Combined Chemotherapy Protocols Neoadjuvant Therapy Biomarkers, Tumor Prognosis Mutation Female High-Throughput Nucleotide Sequencing
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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...
Deep Learning Image Processing, Computer-Assisted Supervised Machine Learning Algorithms Databases, Factual Breast Neoplasms Humans
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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.
Breast Neoplasms Mammography Humans Artificial Intelligence Female
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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 Incidence Humans Risk Factors Breast Neoplasms Machine Learning Middle Aged Young Adult SEER Program Radiotherapy, Adjuvant
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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 ...
Breast Neoplasms Deep Learning Supervised Machine Learning Humans Algorithms Female
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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 Humans Image Processing, Computer-Assisted Female Fuzzy Logic Magnetic Resonance Imaging Breast
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You get the best of both worlds? Integrating deep learning and traditional machine learning for breast cancer risk prediction.

Computers in biology and medicine Feb 5, 2025
Breast Cancer is the most commonly diagnosed cancer worldwide. While screening mammography diminishes the burden of this disease, it has some flaws related to the presence of false negatives. Adapting screening to each woman's needs could help overco...
Humans Machine Learning Female Middle Aged Risk Assessment Case-Control Studies Aged Adult Breast Neoplasms Mammography Deep Learning Early Detection of Cancer
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Leveraging paired mammogram views with deep learning for comprehensive breast cancer detection.

Scientific reports Feb 5, 2025
Employing two standard mammography views is crucial for radiologists, providing comprehensive insights for reliable clinical evaluations. This study introduces paired mammogram view based-network(PMVnet), a novel algorithm designed to enhance breast ...
Deep Learning Humans Mammography Radiographic Image Interpretation, Computer-Assisted Algorithms Female Breast Neoplasms
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