AIMC Topic: Breast Neoplasms

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Breast cancer detection employing stacked ensemble model with convolutional features.

Cancer biomarkers : section A of Disease markers
Breast cancer is a major cause of female deaths, especially in underdeveloped countries. It can be treated if diagnosed early and chances of survival are high if treated appropriately and timely. For timely and accurate automated diagnosis, machine l...

Artificial Intelligence for the Management of Breast Cancer: An Overview.

Current drug discovery technologies
Breast cancer is a severe global health problem, and early detection, accurate diagnosis, and personalized treatment is the key to improving patient outcomes. Artificial intelligence (AI) and machine learning (ML) have emerged as promising breast can...

Subcutaneous fat predicts bone metastasis in breast cancer: A novel multimodality-based deep learning model.

Cancer biomarkers : section A of Disease markers
OBJECTIVES: This study explores a deep learning (DL) approach to predicting bone metastases in breast cancer (BC) patients using clinical information, such as the fat index, and features like Computed Tomography (CT) images.

Learning technology for detection and grading of cancer tissue using tumour ultrasound images1.

Journal of X-ray science and technology
BACKGROUND: Early diagnosis of breast cancer is crucial to perform effective therapy. Many medical imaging modalities including MRI, CT, and ultrasound are used to diagnose cancer.

Think "HER2" different: integrative diagnostic approaches for HER2-low breast cancer.

Pathologica
This work explores the complex field of HER2 testing in the HER2-low breast cancer era, with a focus on methodological aspects. We aim to propose clear positions to scientific societies, institutions, pathologists, and oncologists to guide and shape ...

Prediction of sentinel lymph node metastasis in breast cancer by using deep learning radiomics based on ultrasound images.

Medicine
Sentinel lymph node metastasis (SLNM) is a crucial predictor for breast cancer treatment and survival. This study was designed to propose deep learning (DL) models based on grayscale ultrasound, color Doppler flow imaging (CDFI), and elastography ima...

Moringa oleifera-mediated iron oxide nanoparticles, characterization and their anti-proliferative potential on MDA-MB 231 human breast cancer cells.

Pakistan journal of pharmaceutical sciences
Iron oxide nanoparticles (FeO NPs) stabilized with Moringa oleifera (M.O.) were successfully synthesized. The study aimed to explore the cytotoxic, anti-proliferative and anti-microbial potential of FeO NPs through various assays, including trypan bl...

Mammography Breast Cancer Screening Triage Using Deep Learning: A UK Retrospective Study.

Radiology
Background Breast screening enables early detection of cancers; however, most women have normal mammograms, resulting in repetitive and resource-intensive reading tasks. Purpose To investigate if deep learning (DL) algorithms can be used to triage ma...

Automated exploitation of deep learning for cancer patient stratification across multiple types.

Bioinformatics (Oxford, England)
MOTIVATION: Recent frameworks based on deep learning have been developed to identify cancer subtypes from high-throughput gene expression profiles. Unfortunately, the performance of deep learning is highly dependent on its neural network architecture...