AIMC Topic: Breast Neoplasms

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Optimizing YOLOv11 for automated classification of breast cancer in medical images.

Scientific reports
Breast cancer diagnosis via histopathology image analysis is a complex and subjective process. While deep learning has emerged as a powerful tool for automation, achieving high accuracy across diverse cancer subtypes and magnification levels remains ...

Fractal measures as predictors of histopathological complexity in breast carcinoma mammograms.

Physical biology
This study investigates the efficacy of fractal-based global texture features for distinguishing between malignant and normal mammograms and assessing their potential for molecular subtype differentiation. Digital mammograms were analyzed using stand...

An exploratory study on predicting HER2-positive expression status of breast cancer using ultrasound radiomics combined with machine learning models.

PloS one
OBJECTIVE: This study aimed to investigate the feasibility and potential value of predictive models for human epidermal growth factor receptor 2 (HER2)-positive status in breast cancer (BC) based on radiomics features from conventional ultrasound ima...

A systematic literature review on mammography: deep learning techniques for breast cancer detection with global and Asian perspectives.

BMC cancer
PURPOSE: Breast cancer remains a leading cause of mortality in women worldwide, with notable disparities in incidence and prognosis across regions. This systematic review explores the application of Deep Learning-based computer-aided diagnostic (CAD)...

Enhancement and optimization of a graphene-based biosensing platform using machine learning for precise breast cancer detection.

Scientific reports
In this study, we introduce a machine learning optimized graphene-based biosensor tailored for the early and accurate detection of breast cancer, aiming to elevate diagnostic reliability and clinical efficacy. The device employs a multilayer Ag-SiO₂-...

Subvisual imaging signals as biomarkers of impending lung metastasis: A multicenter pan-cancer study.

European journal of cancer (Oxford, England : 1990)
STUDY AIM: Early detection of distant metastases is crucial, but current imaging detects them only when radiographically visible. This study reported subvisual chest CT signals could serve as early biomarkers for impending lung metastasis before radi...

Single-cell multi-omics uncovers CPS1 as a breast cancer immune evasion therapeutic target.

Scientific reports
Despite significant advances in early detection and therapeutic interventions, breast cancer persists as the most frequently diagnosed malignancy and the leading cause of cancer-related deaths among women globally. Although multiple prognostic signat...

Analysis of Breast Cancer Information on Facebook Using Neural Network-Based Topic Modeling and Metadata Analysis of English and Spanish Content: Comparative Study.

Journal of medical Internet research
BACKGROUND: Breast cancer is the most common cancer diagnosis among women, with approximately 2.3 million new cases annually. When faced with a cancer diagnosis, individuals often turn to the internet for information or reassurance, despite the risk ...

Machine and deep learning applied to medical microwave imaging: a scoping review from reconstruction to classification.

Progress in biomedical engineering (Bristol, England)
Microwave imaging (MWI) is a promising modality due to its non-invasive nature and lower cost compared to other medical imaging techniques. These characteristics make it a potential alternative to traditional imaging techniques. It has various medica...

Integrated Chemical Array and SERS Profiling of Plasma Small Extracellular Vesicles for Breast Cancer Diagnosis.

Nano letters
Small extracellular vesicles (sEVs) are nanoscale vesicles carrying biomolecules reflective of their cellular origin, making them attractive biomarkers for cancer diagnosis. In this study, we present a high-throughput strategy integrating amphiphile-...