AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Breast

Showing 151 to 160 of 574 articles

Clear Filters

Two-stage classification strategy for breast cancer diagnosis using ultrasound-guided diffuse optical tomography and deep learning.

Journal of biomedical optics
SIGNIFICANCE: Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated great potential for breast cancer diagnosis in which real-time or near real-time diagnosis with high accuracy is desired.

3D Breast Cancer Segmentation in DCE-MRI Using Deep Learning With Weak Annotation.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Deep learning models require large-scale training to perform confidently, but obtaining annotated datasets in medical imaging is challenging. Weak annotation has emerged as a way to save time and effort.

A Novel Artificial Intelligence Model for Symmetry Evaluation in Breast Cancer Patients.

Aesthetic plastic surgery
INTRODUCTION: Artificial intelligence (AI) is a milestone for human technology. In medicine, AI is set to play an important role as we progress into a new era. In plastic surgery, AI can participate in breast symmetry assessment, which until now has ...

Mammography using low-frequency electromagnetic fields with deep learning.

Scientific reports
In this paper, a novel technique for detecting female breast anomalous tissues is presented and validated through numerical simulations. The technique, to a high degree, resembles X-ray mammography; however, instead of using X-rays for obtaining imag...

Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective.

Computers in biology and medicine
Artificial intelligence (AI) in healthcare plays a pivotal role in combating many fatal diseases, such as skin, breast, and lung cancer. AI is an advanced form of technology that uses mathematical-based algorithmic principles similar to those of the ...

Enhanced breast mass mammography classification approach based on pre-processing and hybridization of transfer learning models.

Journal of cancer research and clinical oncology
BACKGROUND AND OBJECTIVE: The second most prevalent cause of death among women is now breast cancer, surpassing heart disease. Mammography images must accurately identify breast masses to diagnose early breast cancer, which can significantly increase...

The Clinical Added Value of Breast Cancer Imaging Using Hybrid PET/MR Imaging.

Magnetic resonance imaging clinics of North America
Dedicated MR imaging is highly performant for the evaluation of the primary lesion and should regularly be added to whole-body PET/MR imaging for the initial staging. PET/MR imaging is highly sensitive for the detection of nodal involvement and could...

Breast Tumor Segmentation in DCE-MRI With Tumor Sensitive Synthesis.

IEEE transactions on neural networks and learning systems
Segmenting breast tumors from dynamic contrast-enhanced magnetic resonance (DCE-MR) images is a critical step for early detection and diagnosis of breast cancer. However, variable shapes and sizes of breast tumors, as well as inhomogeneous background...