BACKGROUND: To compare the breast cancer detection performance in digital mammograms of a panel of three unaided human readers (HR) versus a stand-alone artificial intelligence (AI)-based Transpara system in a population of Japanese women.
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Feb 10, 2020
Breast cancer accounts for the second-largest number of deaths in women around the world, and more than 8% of women will suffer from the disease in their lifetime. Mortality due to breast cancer can be reduced by its early and precise diagnosis. Many...
BACKGROUND AND PURPOSE: Multiparametric radiological imaging is vital for detection, characterization, and diagnosis of many different diseases. Radiomics provide quantitative metrics from radiological imaging that may infer potential biological mean...
Accurate tracking of tissue motion is critically important for several ultrasound elastography methods. In this study, we investigate the feasibility of using three published convolution neural network (CNN) models built for optical flow (hereafter r...
Computational and mathematical methods in medicine
Jan 28, 2020
To improve the automatic segmentation accuracy of breast masses in digital breast tomosynthesis (DBT) images, we propose a DBT mass automatic segmentation algorithm by using a U-Net architecture. Firstly, to suppress the background tissue noise and e...
A deep learning (DL) network for 2D-based breast mass segmentation in unenhanced dedicated breast CT images was developed and validated, and its robustness in radiomic feature stability and diagnostic performance compared to manual annotations of mul...
Computer methods and programs in biomedicine
Jan 25, 2020
BACKGROUND AND OBJECTIVES: Automated breast ultrasound (ABUS) is a widely used screening modality for breast cancer detection and diagnosis. In this study, an effective and fast computer-aided detection (CADe) system based on a 3-D convolutional neur...
Although computers have had a role in interpretation of mammograms for at least two decades, their impact on performance has not lived up to expectations. However, in the last five years, the field of medical image analysis has undergone a revolution...
BMC medical informatics and decision making
Dec 30, 2019
BACKGROUND: There are often multiple lesions in breast magnetic resonance imaging (MRI) reports and radiologists usually focus on describing the index lesion that is most crucial to clinicians in determining the management and prognosis of patients. ...
Deep learning approaches are widely applied to histopathological image analysis due to the impressive levels of performance achieved. However, when dealing with high-resolution histopathological images, utilizing the original image as input to the de...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.