AIMC Topic: Pattern Recognition, Automated

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Characterization of Adrenal Lesions on Unenhanced MRI Using Texture Analysis: A Machine-Learning Approach.

Journal of magnetic resonance imaging : JMRI Jan 17, 2018
BACKGROUND: Adrenal adenomas (AA) are the most common benign adrenal lesions, often characterized based on intralesional fat content as either lipid-rich (LRA) or lipid-poor (LPA). The differentiation of AA, particularly LPA, from nonadenoma adrenal ...
Young Adult Adult Male Middle Aged Female Reproducibility of Results Aged Magnetic Resonance Imaging Lipids Adenoma Adrenal Gland Neoplasms Adolescent Image Processing, Computer-Assisted Contrast Media Humans Machine Learning Algorithms Retrospective Studies Image Interpretation, Computer-Assisted Pattern Recognition, Automated Adrenal Glands
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Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity.

Human brain mapping Jan 16, 2018
Despite significant progress in the field, the detection of fMRI signal changes during hallucinatory events remains difficult and time-consuming. This article first proposes a machine-learning algorithm to automatically identify resting-state fMRI pe...
Neural Pathways Hallucinations Auditory Perception Principal Component Analysis Schizophrenia Humans Pattern Recognition, Automated Machine Learning Neurofeedback Male Brain Magnetic Resonance Imaging Female Brain Mapping Adult
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Taxonomy of multi-focal nematode image stacks by a CNN based image fusion approach.

Computer methods and programs in biomedicine Jan 11, 2018
BACKGROUND AND OBJECTIVE: In the biomedical field, digital multi-focal images are very important for documentation and communication of specimen data, because the morphological information for a transparent specimen can be captured in form of a stack...
Linear Models Sensitivity and Specificity Neural Networks, Computer Pattern Recognition, Automated Animals Algorithms Nematoda Image Processing, Computer-Assisted Reproducibility of Results Support Vector Machine
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Unsupervised Learning and Pattern Recognition of Biological Data Structures with Density Functional Theory and Machine Learning.

Scientific reports Jan 11, 2018
By introducing the methods of machine learning into the density functional theory, we made a detour for the construction of the most probable density function, which can be estimated by learning relevant features from the system of interest. Using th...
Unsupervised Machine Learning Pattern Recognition, Automated Humans Brain Neoplasms Image Processing, Computer-Assisted Magnetic Resonance Imaging
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A wrapped multi-label classifier for the automatic diagnosis and prognosis of Alzheimer's disease.

Journal of neuroscience methods Jan 9, 2018
BACKGROUND: AD is the most frequent neurodegenerative disease, severely impacting our society. Early diagnosis and prognosis are challenging tasks in the management of AD patients.
Female Male Magnetic Resonance Imaging Alzheimer Disease Image Interpretation, Computer-Assisted Humans Pattern Recognition, Automated Machine Learning Brain Prognosis
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Machine-learning neuroimaging challenge for automated diagnosis of mild cognitive impairment: Lessons learnt.

Journal of neuroscience methods Jan 2, 2018
Humans Machine Learning Alzheimer Disease Image Interpretation, Computer-Assisted Pattern Recognition, Automated Neuroimaging Databases, Factual Brain Disease Progression Cognitive Dysfunction
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Lung Lesion Detection in CT Scan Images Using the Fuzzy Local Information Cluster Means (FLICM) Automatic Segmentation Algorithm and Back Propagation Network Classification.

Asian Pacific journal of cancer prevention : APJCP Dec 29, 2017
Lung cancer is a frequently lethal disease often causing death of human beings at an early age because of uncontrolled cell growth in the lung tissues. The diagnostic methods available are less than effective for detection of cancer. Therefore an aut...
Pattern Recognition, Automated Algorithms Lung Neoplasms Tomography, X-Ray Computed Fuzzy Logic Diagnosis, Computer-Assisted Humans Radiographic Image Interpretation, Computer-Assisted Neural Networks, Computer Image Processing, Computer-Assisted
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Novel real-time tumor-contouring method using deep learning to prevent mistracking in X-ray fluoroscopy.

Radiological physics and technology Dec 28, 2017
Robustness to obstacles is the most important factor necessary to achieve accurate tumor tracking without fiducial markers. Some high-density structures, such as bone, are enhanced on X-ray fluoroscopic images, which cause tumor mistracking. Tumor tr...
Tomography, X-Ray Computed Lung Neoplasms Radiographic Image Interpretation, Computer-Assisted Fluoroscopy Pattern Recognition, Automated X-Rays Fiducial Markers Humans Machine Learning Algorithms
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Deep learning reveals Alzheimer's disease onset in MCI subjects: Results from an international challenge.

Journal of neuroscience methods Dec 26, 2017
BACKGROUND: Early diagnosis of Alzheimer's disease (AD) and its onset in subjects affected by mild cognitive impairment (MCI) based on structural MRI features is one of the most important open issues in neuroimaging. Accordingly, a scientific challen...
Image Interpretation, Computer-Assisted Deep Learning Pattern Recognition, Automated Cognitive Dysfunction Magnetic Resonance Imaging Alzheimer Disease Humans Brain Disease Progression
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Neuroanatomical heterogeneity of schizophrenia revealed by semi-supervised machine learning methods.

Schizophrenia research Dec 21, 2017
UNLABELLED: Schizophrenia is associated with heterogeneous clinical symptoms and neuroanatomical alterations. In this work, we aim to disentangle the patterns of neuroanatomical alterations underlying a heterogeneous population of patients using a se...
Magnetic Resonance Imaging Supervised Machine Learning Humans Schizophrenia Pattern Recognition, Automated Brain Male Female Cohort Studies Young Adult Image Processing, Computer-Assisted Adult Multivariate Analysis
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