AI Medical Compendium Topic:
Breast Neoplasms

Clear Filters Showing 811 to 820 of 2087 articles

Visual and quantitative evaluation of microcalcifications in mammograms with deep learning-based super-resolution.

European journal of radiology
PURPOSE: To evaluate visually and quantitatively the performance of a deep-learning-based super-resolution (SR) model for microcalcifications in digital mammography.

Breast Cancer Detection on Histopathological Images Using a Composite Dilated Backbone Network.

Computational intelligence and neuroscience
Breast cancer is a lethal illness that has a high mortality rate. In treatment, the accuracy of diagnosis is crucial. Machine learning and deep learning may be beneficial to doctors. The proposed backbone network is critical for the present performan...

Breast Cancer Detection and Classification Empowered With Transfer Learning.

Frontiers in public health
Cancer is a major public health issue in the modern world. Breast cancer is a type of cancer that starts in the breast and spreads to other parts of the body. One of the most common types of cancer that kill women is breast cancer. When cells become ...

Improving Breast Tumor Segmentation in PET via Attentive Transformation Based Normalization.

IEEE journal of biomedical and health informatics
Positron Emission Tomography (PET) has become a preferred imaging modality for cancer diagnosis, radiotherapy planning, and treatment responses monitoring. Accurate and automatic tumor segmentation is the fundamental requirement for these clinical ap...

A Convolutional Neural Network and Graph Convolutional Network Based Framework for Classification of Breast Histopathological Images.

IEEE journal of biomedical and health informatics
The spatial correlation among different tissue components is an essential characteristic for diagnosis of breast cancers based on histopathological images. Graph convolutional network (GCN) can effectively capture this spatial feature representation,...

Combined diagnosis of multiparametric MRI-based deep learning models facilitates differentiating triple-negative breast cancer from fibroadenoma magnetic resonance BI-RADS 4 lesions.

Journal of cancer research and clinical oncology
PURPOSE: To investigate the value of the combined diagnosis of multiparametric MRI-based deep learning models to differentiate triple-negative breast cancer (TNBC) from fibroadenoma magnetic resonance Breast Imaging-Reporting and Data System category...

A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms.

Sensors (Basel, Switzerland)
One of the most promising research areas in the healthcare industry and the scientific community is focusing on the AI-based applications for real medical challenges such as the building of computer-aided diagnosis (CAD) systems for breast cancer. Tr...

Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection.

BioMed research international
Breast cancer is the most common cancer in women, and the breast mass recognition model can effectively assist doctors in clinical diagnosis. However, the scarcity of medical image samples makes the recognition model prone to overfitting. A breast ma...

A Novel Hybrid Deep Learning Model for Metastatic Cancer Detection.

Computational intelligence and neuroscience
Cancer has been found as a heterogeneous disease with various subtypes and aims to destroy the body's normal cells abruptly. As a result, it is essential to detect and prognosis the distinct type of cancer since they may help cancer survivors with tr...

Possible strategies for use of artificial intelligence in screen-reading of mammograms, based on retrospective data from 122,969 screening examinations.

European radiology
OBJECTIVES: Artificial intelligence (AI) has shown promising results when used on retrospective data from mammographic screening. However, few studies have explored the possible consequences of different strategies for combining AI and radiologists i...