AIMC Topic: Female

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Learning from adversarial medical images for X-ray breast mass segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Simulation of diverse lesions in images is proposed and applied to overcome the scarcity of labeled data, which has hindered the application of deep learning in medical imaging. However, most of current studies focus on gene...

Psychological reactions to human versus robotic job replacement.

Nature human behaviour
Advances in robotics and artificial intelligence are increasingly enabling organizations to replace humans with intelligent machines and algorithms. Forecasts predict that, in the coming years, these new technologies will affect millions of workers i...

Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images.

European radiology experimental
BACKGROUND: The purpose of this work was to evaluate computable Breast Imaging Reporting and Data System (BI-RADS) radiomic features to classify breast masses on ultrasound B-mode images.

Community-dwelling older adults' needs and acceptance regarding the use of robot technology to assist with daily living performance.

BMC geriatrics
BACKGROUND: The rate of aging in Korea is extremely fast compared to major countries. We examined the key demands of community-dwelling older adults with regard to Connected Active Space technology, which provides tailored assistance with daily livin...

Machine learning to predict cardiovascular risk.

International journal of clinical practice
AIMS: To analyse the predictive capacity of 15 machine learning methods for estimating cardiovascular risk in a cohort and to compare them with other risk scales.

Detection of major depressive disorder from linear and nonlinear heart rate variability features during mental task protocol.

Computers in biology and medicine
BACKGROUND: Major depressive disorder (MDD) is one of the leading causes of disability; however, current MDD diagnosis methods lack an objective assessment of depressive symptoms. Here, a machine learning approach to separate MDD patients from health...

Accurate detection of atrial fibrillation from 12-lead ECG using deep neural network.

Computers in biology and medicine
Atrial fibrillation (AF) is the most common heart arrhythmia, and 12-lead electrocardiogram (ECG) is regarded as the gold standard for AF diagnosis. Highly accurate diagnosis of AF based on 12-lead ECG is valuable and remains challenging. In this pap...

Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning.

Neuroradiology
PURPOSE: Pituitary adenomas are among the most frequent intracranial tumors. They may exhibit clinically aggressive behavior, with recurrent disease and resistance to multimodal therapy. The ki-67 labeling index represents a proliferative marker whic...