AIMC Topic: Neural Networks, Computer

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Optimizing MRF-ASL scan design for precise quantification of brain hemodynamics using neural network regression.

Magnetic resonance in medicine
PURPOSE: Arterial Spin Labeling (ASL) is a quantitative, non-invasive alternative for perfusion imaging that does not use contrast agents. The magnetic resonance fingerprinting (MRF) framework can be adapted to ASL to estimate multiple physiological ...

QSMGAN: Improved Quantitative Susceptibility Mapping using 3D Generative Adversarial Networks with increased receptive field.

NeuroImage
Quantitative susceptibility mapping (QSM) is a powerful MRI technique that has shown great potential in quantifying tissue susceptibility in numerous neurological disorders. However, the intrinsic ill-posed dipole inversion problem greatly affects th...

Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: Application to epilepsy lesion screening.

Medical image analysis
In this study, we propose a novel anomaly detection model targeting subtle brain lesions in multiparametric MRI. To compensate for the lack of annotated data adequately sampling the heterogeneity of such pathologies, we cast this problem as an outlie...

Detection of sunn pest-damaged wheat grains using artificial bee colony optimization-based artificial intelligence techniques.

Journal of the science of food and agriculture
BACKGROUND: In this study, artificial intelligence models that identify sunn pest-damaged wheat grains (SDG) and healthy wheat grains (HWG) are presented. Svevo durum wheat cultivated in Konya province, Turkey is used for the process, with 150 HWG an...

The Effects of Population Tuning and Trial-by-Trial Variability on Information Encoding and Behavior.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Identifying the features of population responses that are relevant to the amount of information encoded by neuronal populations is a crucial step toward understanding population coding. Statistical features, such as tuning properties, individual and ...

DESIGN AND DEVELOPMENT OF HUMAN COMPUTER INTERFACE USING ELECTROOCULOGRAM WITH DEEP LEARNING.

Artificial intelligence in medicine
Today's life assistive devices were playing significant role in our life to communicate with others. In that modality Human Computer Interface (HCI) based Electrooculogram (EOG) playing vital part. By using this method we can able to overcome the con...

Using the Chou's 5-steps rule to predict splice junctions with interpretable bidirectional long short-term memory networks.

Computers in biology and medicine
Neural models have been able to obtain state-of-the-art performances on several genome sequence-based prediction tasks. Such models take only nucleotide sequences as input and learn relevant features on their own. However, extracting the interpretabl...

Convolutional Neural Networks for the Design and Analysis of Non-Fullerene Acceptors.

Journal of chemical information and modeling
Convolutional neural network (CNN) is employed to construct generative and prediction models for the design and analysis of non-fullerene acceptors (NFAs) in organic solar cells. It is demonstrated that the dilated causal CNN can be trained as a good...

Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data.

BMC medical informatics and decision making
BACKGROUND: Neuropsychological tests (NPTs) are important tools for informing diagnoses of cognitive impairment (CI). However, interpreting NPTs requires specialists and is thus time-consuming. To streamline the application of NPTs in clinical settin...

Data-driven analyses of motor impairments in animal models of neurological disorders.

PLoS biology
Behavior provides important insights into neuronal processes. For example, analysis of reaching movements can give a reliable indication of the degree of impairment in neurological disorders such as stroke, Parkinson disease, or Huntington disease. T...