BMC medical informatics and decision making
Mar 22, 2019
BACKGROUND: Breast cancer is one of the most common diseases in women worldwide. Many studies have been conducted to predict the survival indicators, however most of these analyses were predominantly performed using basic statistical methods. As an a...
PURPOSE: To develop and validate an interpretable and repeatable machine learning model approach to predict molecular subtypes of breast cancer from clinical metainformation together with mammography and MRI images.
PURPOSE: We aimed to use deep learning with convolutional neural network (CNN) to discriminate between benign and malignant breast mass images from ultrasound.
BACKGROUND: Following visible successes on a wide range of predictive tasks, machine learning techniques are attracting substantial interest from medical researchers and clinicians. We address the need for capacity development in this area by providi...
This article reviews current limitations and future opportunities for the application of computer-aided detection (CAD) systems and artificial intelligence in breast imaging. Traditional CAD systems in mammography screening have followed a rules-base...
Diagnostic and interventional imaging
Mar 15, 2019
PURPOSE: The goal of this data challenge was to create a structured dynamic with the following objectives: (1) teach radiologists the new rules of General Data Protection Regulation (GDPR), while building a large multicentric prospective database of ...
Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis, and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse da...
Automated cell classification is an important yet a challenging computer vision task with significant benefits to biomedicine. In recent years, there have been several studies attempted to build an artificial intelligence-based cell classifier using ...
OBJECTIVES: The aim of this study was to develop a fully automated deep learning approach for identification of the pectoral muscle on mediolateral oblique (MLO) view mammograms and evaluate its performance in comparison to our previously developed t...
Nonlinear fuzzy classification models have better classification performance than linear fuzzy classifiers. In many nonlinear fuzzy classification problems, piecewise-linear fuzzy discriminant functions can approximate nonlinear fuzzy discriminant fu...
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