AIMC Topic: Young Adult
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Noise-trained deep neural networks effectively predict human vision and its neural responses to challenging images.
PLoS biology
Dec 9, 2021
Deep neural networks (DNNs) for object classification have been argued to provide the most promising model of the visual system, accompanied by claims that they have attained or even surpassed human-level performance. Here, we evaluated whether DNNs ...
Convolutional Neural Network With Graphical Lasso to Extract Sparse Topological Features for Brain Disease Classification.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 8, 2021
The functional connectivity provides new insights into the mechanisms of the human brain at network-level, which has been proved to be an effective biomarker for brain disease classification. Recently, machine learning methods have played an importan...
Application of information theoretic feature selection and machine learning methods for the development of genetic risk prediction models.
Scientific reports
Dec 2, 2021
In view of the growth of clinical risk prediction models using genetic data, there is an increasing need for studies that use appropriate methods to select the optimum number of features from a large number of genetic variants with a high degree of r...
Fully automatic segmentation of the mandible based on convolutional neural networks (CNNs).
Orthodontics & craniofacial research
Dec 1, 2021
OBJECTIVES: To evaluate the accuracy of automatic deep learning-based method for fully automatic segmentation of the mandible from CBCTs.
A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD.
NeuroImage
Nov 30, 2021
The pathological mechanism of attention deficit hyperactivity disorder (ADHD) is incompletely specified, which leads to difficulty in precise diagnosis. Functional magnetic resonance imaging (fMRI) has emerged as a common neuroimaging technique for s...
Deep learning-based quantification of temporalis muscle has prognostic value in patients with glioblastoma.
British journal of cancer
Nov 30, 2021
BACKGROUND: Glioblastoma is the commonest malignant brain tumour. Sarcopenia is associated with worse cancer survival, but manually quantifying muscle on imaging is time-consuming. We present a deep learning-based system for quantification of tempora...
A non-invasive, automated diagnosis of Menière's disease using radiomics and machine learning on conventional magnetic resonance imaging: A multicentric, case-controlled feasibility study.
La Radiologia medica
Nov 25, 2021
PURPOSE: This study investigated the feasibility of a new image analysis technique (radiomics) on conventional MRI for the computer-aided diagnosis of Menière's disease.
Neural correlates of word representation vectors in natural language processing models: Evidence from representational similarity analysis of event-related brain potentials.
Psychophysiology
Nov 24, 2021
Natural language processing models based on machine learning (ML-NLP models) have been developed to solve practical problems, such as interpreting an Internet search query. These models are not intended to reflect human language comprehension mechani...
Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization.
Scientific reports
Nov 23, 2021
Atrial fibrillation (AF) is an abnormal heart rhythm, asymptomatic in many cases, that causes several health problems and mortality in population. This retrospective study evaluates the ability of different AI-based models to predict future episodes ...
Network analysis of trauma in patients with early-stage psychosis.
Scientific reports
Nov 23, 2021
Childhood trauma (ChT) is a risk factor for psychosis. Negative lifestyle factors such as rumination, negative schemas, and poor diet and exercise are common in psychosis. The present study aimed to perform a network analysis of interactions between ...
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