AIMC Topic: Breast Neoplasms

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Multimodal deep learning model for prediction of breast cancer recurrence risk and correlation with oncotype DX.

Breast cancer research : BCR
BACKGROUND: Proper stratification of recurrence risk in breast cancer is crucial for guiding treatment decisions. This study aims to predict the recurrence risk of breast cancer patients using a multimodal deep learning model that integrates multiple...

Prediction of intraductal cancer microinfiltration based on the hierarchical fusion of peri-tumor imaging histology and dual view deep learning.

BMC cancer
OBJECTIVE: The aim of this study was to develop a multimodal fusion model for accurate risk prediction and clinical decision support for ductal carcinoma in-situ (DCIS).

BCECNN: an explainable deep ensemble architecture for accurate diagnosis of breast cancer.

BMC medical informatics and decision making
BACKGROUND: Breast cancer remains one of the leading causes of cancer-related deaths globally, affecting both women and men. This study aims to develop a novel deep learning (DL)-based architecture, the Breast Cancer Ensemble Convolutional Neural Net...

A mixture of experts (MoE) model to improve AI-based computational pathology prediction performance under variable levels of image blur.

BMC medical imaging
BACKGROUND: AI-based models for analysis of histopathology whole slide images (WSIs) are now common. However, image quality, particularly unsharp areas of WSIs, impacts model performance. In this study we investigate the impact of blur on deep learni...

Assessment of an unsupervised denoising approach based on Noise2Void in digital mammography.

Scientific reports
Full-field digital mammography (FFDM) is the most common imaging technique for breast cancer screening programs. Still, it is limited by noise from quantum effects, electronic issues, and X-ray scattering, affecting the image quality. Traditional den...

Sustainable deep learning-based breast lesion segmentation: impact of breast region segmentation on performance.

BMC medical imaging
PURPOSE: Segmentation of breast lesions in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is critical for effective diagnosis. This study investigates the impact of breast region segmentation (BRS) on the performance of deep learning-...

Machine learning analysis of coagulation-related genes for breast cancer diagnosis and prognosis prediction.

Scientific reports
The purpose of this study was to investigate the relationship between coagulation related genes (CRGs) and breast cancer (BC). First, we found that most CRGs are abnormally expressed in BC patients and correlated with their prognosis. Therefore, we e...

Machine Learning-Enhanced Analysis of miRNA Biomarkers for Accurate Breast Cancer Diagnosis Using DNA Seagrass.

Analytical chemistry
As potential biomarkers for breast cancer, microRNAs (miRNAs) have demonstrated significant promise in clinical applications. However, accurate miRNA-based breast cancer diagnosis is hindered by the lack of simple, ultrasensitive, and highly specific...

Optimizing breast cancer chemotherapy by harnessing gut microbiota with insights from artificial intelligence.

NPJ biofilms and microbiomes
Optimizing chemotherapy for breast cancer (BC) remains a critical challenge. Gut microbiota (GM) dysbiosis, varying across BC subtypes and stages, influences BC development and chemotherapy response through immune and metabolic pathways. Chemotherapy...

TransBreastNet a CNN transformer hybrid deep learning framework for breast cancer subtype classification and temporal lesion progression analysis.

Scientific reports
Breast cancer continues to be a global public health challenge. An early and precise diagnosis is crucial for improving prognosis and efficacy. While deep learning (DL) methods have shown promising advances in breast cancer classification from mammog...