AI Medical Compendium Topic:
Breast Neoplasms

Clear Filters Showing 791 to 800 of 2087 articles

Global research trends and foci of artificial intelligence-based tumor pathology: a scientometric study.

Journal of translational medicine
BACKGROUND: With the development of digital pathology and the renewal of deep learning algorithm, artificial intelligence (AI) is widely applied in tumor pathology. Previous researches have demonstrated that AI-based tumor pathology may help to solve...

Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images.

Scientific reports
Breast cancer tumor grade is strongly associated with patient survival. In current clinical practice, pathologists assign tumor grade after visual analysis of tissue specimens. However, different studies show significant inter-observer variation in b...

Breast cancer detection using deep learning: Datasets, methods, and challenges ahead.

Computers in biology and medicine
Breast Cancer (BC) is the most commonly diagnosed cancer and second leading cause of mortality among women. About 1 in 8 US women (about 13%) will develop invasive BC throughout their lifetime. Early detection of this life-threatening disease not onl...

Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM).

Computational intelligence and neuroscience
Breast cancer is common among women all over the world. Early identification of breast cancer lowers death rates. However, it is difficult to determine whether these are cancerous or noncancerous lesions due to their inconsistencies in image appearan...

Application of Deep Learning to Reduce the Rate of Malignancy Among BI-RADS 4A Breast Lesions Based on Ultrasonography.

Ultrasound in medicine & biology
The aim of the work described here was to develop an ultrasound (US) image-based deep learning model to reduce the rate of malignancy among breast lesions diagnosed as category 4A of the Breast Imaging-Reporting and Data System (BI-RADS) during the p...

Application of Feature Selection and Deep Learning for Cancer Prediction Using DNA Methylation Markers.

Genes
DNA methylation is a process that can affect gene accessibility and therefore gene expression. In this study, a machine learning pipeline is proposed for the prediction of breast cancer and the identification of significant genes that contribute to t...

Artificial intelligence computer-aided detection enhances synthesized mammograms: comparison with original digital mammograms alone and in combination with tomosynthesis images in an experimental setting.

Breast cancer (Tokyo, Japan)
BACKGROUND: It remains unclear whether original full-field digital mammograms (DMs) can be replaced with synthesized mammograms in both screening and diagnostic settings. To compare reader performance of artificial intelligence computer-aided detecti...

Domain generalization in deep learning based mass detection in mammography: A large-scale multi-center study.

Artificial intelligence in medicine
Computer-aided detection systems based on deep learning have shown great potential in breast cancer detection. However, the lack of domain generalization of artificial neural networks is an important obstacle to their deployment in changing clinical ...

Antenna Excitation Optimization with Deep Learning for Microwave Breast Cancer Hyperthermia.

Sensors (Basel, Switzerland)
Microwave hyperthermia (MH) requires the effective calibration of antenna excitations for the selective focusing of the microwave energy on the target region, with a nominal effect on the surrounding tissue. To this end, many different antenna calibr...

The potential of predictive and prognostic breast MRI (P2-bMRI).

European radiology experimental
Magnetic resonance imaging (MRI) is an important part of breast cancer diagnosis and multimodal workup. It provides unsurpassed soft tissue contrast to analyse the underlying pathophysiology, and it is adopted for a variety of clinical indications. P...