AIMC Topic: Humans

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Building machine learning models without sharing patient data: A simulation-based analysis of distributed learning by ensembling.

Journal of biomedical informatics
The development of machine learning solutions in medicine is often hindered by difficulties associated with sharing patient data. Distributed learning aims to train machine learning models locally without requiring data sharing. However, the utility ...

Analyzing the Effectiveness of the Brain-Computer Interface for Task Discerning Based on Machine Learning.

Sensors (Basel, Switzerland)
The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using ...

Facial Expressions Recognition for Human-Robot Interaction Using Deep Convolutional Neural Networks with Rectified Adam Optimizer.

Sensors (Basel, Switzerland)
The interaction between humans and an NAO robot using deep convolutional neural networks (CNN) is presented in this paper based on an innovative end-to-end pipeline method that applies two optimized CNNs, one for face recognition (FR) and another one...

Gait-Based Machine Learning for Classifying Patients with Different Types of Mild Cognitive Impairment.

Journal of medical systems
Mild cognitive impairment (MCI) may be caused by Alzheimer's disease, Parkinson's disease (PD), cerebrovascular accident, nutritional or metabolic disorders, or mental disorders. It is important to determine the cause and treatment of dementia as ear...

Brain experiments imply adaptation mechanisms which outperform common AI learning algorithms.

Scientific reports
Attempting to imitate the brain's functionalities, researchers have bridged between neuroscience and artificial intelligence for decades; however, experimental neuroscience has not directly advanced the field of machine learning (ML). Here, using neu...

Single patient convolutional neural networks for real-time MR reconstruction: coherent low-resolution versus incoherent undersampling.

Physics in medicine and biology
Accelerated MRI involves undersampling k-space, creating unwanted artifacts when reconstructing the data. While the strategy of incoherent k-space acquisition is proven for techniques such as compressed sensing, it may not be optimal for all techniqu...

Learning-based local-to-global landmark annotation for automatic 3D cephalometry.

Physics in medicine and biology
The annotation of three-dimensional (3D) cephalometric landmarks in 3D computerized tomography (CT) has become an essential part of cephalometric analysis, which is used for diagnosis, surgical planning, and treatment evaluation. The automation of 3D...