AIMC Topic: Humans

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Improving the Antinoise Ability of DNNs via a Bio-Inspired Noise Adaptive Activation Function Rand Softplus.

Neural computation Apr 12, 2019
Although deep neural networks (DNNs) have led to many remarkable results in cognitive tasks, they are still far from catching up with human-level cognition in antinoise capability. New research indicates how brittle and susceptible current models are...
Neural Networks, Computer Computer Simulation Models, Biological Pattern Recognition, Automated Photic Stimulation Humans
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Learning Moral Graphs in Construction of High-Dimensional Bayesian Networks for Mixed Data.

Neural computation Apr 12, 2019
Bayesian networks have been widely used in many scientific fields for describing the conditional independence relationships for a large set of random variables. This letter proposes a novel algorithm, the so-called -learning algorithm, for learning m...
Bayes Theorem Neural Networks, Computer Algorithms Humans
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Neural transfer learning for assigning diagnosis codes to EMRs.

Artificial intelligence in medicine Apr 12, 2019
OBJECTIVE: Electronic medical records (EMRs) are manually annotated by healthcare professionals and specialized medical coders with a standardized set of alphanumeric diagnosis and procedure codes, specifically from the International Classification o...
Humans Electronic Health Records Neural Networks, Computer International Classification of Diseases Clinical Coding
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Machine learning-assisted directed protein evolution with combinatorial libraries.

Proceedings of the National Academy of Sciences of the United States of America Apr 12, 2019
To reduce experimental effort associated with directed protein evolution and to explore the sequence space encoded by mutating multiple positions simultaneously, we incorporate machine learning into the directed evolution workflow. Combinatorial sequ...
Humans Machine Learning Models, Molecular Amino Acid Sequence Protein Conformation Directed Molecular Evolution Oxygenases Rhodothermus Small Molecule Libraries Combinatorial Chemistry Techniques
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Automatic PET cervical tumor segmentation by combining deep learning and anatomic prior.

Physics in medicine and biology Apr 12, 2019
Cervical tumor segmentation on 3D FDG PET images is a challenging task because of the proximity between cervix and bladder, both of which can uptake FDG tracers. This problem makes traditional segmentation based on intensity variation methods ineffec...
Deep Learning Imaging, Three-Dimensional Image Processing, Computer-Assisted Humans Female Uterine Cervical Neoplasms
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Microstimulation in a spiking neural network model of the midbrain superior colliculus.

PLoS computational biology Apr 12, 2019
The midbrain superior colliculus (SC) generates a rapid saccadic eye movement to a sensory stimulus by recruiting a population of cells in its topographically organized motor map. Supra-threshold electrical microstimulation in the SC reveals that the...
Saccades Superior Colliculi Haplorhini Electric Stimulation Animals Computational Biology Models, Neurological Action Potentials Neurons Nerve Net Humans Neural Networks, Computer
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Information Transmitted From Bioinspired Neuron-Astrocyte Network Improves Cortical Spiking Network's Pattern Recognition Performance.

IEEE transactions on neural networks and learning systems Apr 11, 2019
We trained two spiking neural networks (SNNs), the cortical spiking network (CSN) and the cortical neuron-astrocyte network (CNAN), using a spike-based unsupervised method, on the MNIST and alpha-digit data sets and achieve an accuracy of 96.1% and 7...
Neurons Pattern Recognition, Automated Cerebral Cortex Humans Action Potentials Electroencephalography Phase Synchronization Machine Learning Astrocytes Neural Networks, Computer Alpha Rhythm Algorithms Computer Simulation
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Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT.

European radiology Apr 11, 2019
OBJECTIVES: Deep learning reconstruction (DLR) is a new reconstruction method; it introduces deep convolutional neural networks into the reconstruction flow. This study was conducted in order to examine the clinical applicability of abdominal ultra-h...
Humans Female Reproducibility of Results Aged Algorithms Retrospective Studies Tomography, X-Ray Computed Liver Neoplasms Abdomen Radiographic Image Interpretation, Computer-Assisted Male Middle Aged Deep Learning Aged, 80 and over Adult
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Learning to Reconstruct Computed Tomography Images Directly From Sinogram Data Under A Variety of Data Acquisition Conditions.

IEEE transactions on medical imaging Apr 11, 2019
Computed tomography (CT) is widely used in medical diagnosis and non-destructive detection. Image reconstruction in CT aims to accurately recover pixel values from measured line integrals, i.e., the summed pixel values along straight lines. Provided ...
Head Radiography, Thoracic Phantoms, Imaging Abdomen Coronary Vessels Tomography, X-Ray Computed Humans Deep Learning Image Processing, Computer-Assisted
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Lungs nodule detection framework from computed tomography images using support vector machine.

Microscopy research and technique Apr 11, 2019
The emergence of cloud infrastructure has the potential to provide significant benefits in a variety of areas in the medical imaging field. The driving force behind the extensive use of cloud infrastructure for medical image processing is the exponen...
Lung Neoplasms Support Vector Machine False Positive Reactions Lung Image Processing, Computer-Assisted Humans Tomography, X-Ray Computed Sensitivity and Specificity
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