AIMC Topic: Neural Networks, Computer

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A Convolutional Neural Network Model for Distinguishing Hemangioblastomas From Other Cerebellar-and-Brainstem Tumors Using Contrast-Enhanced MRI.

Journal of magnetic resonance imaging : JMRI Jan 11, 2024
BACKGROUND: Hemangioblastoma (HB) is a highly vascularized tumor most commonly occurring in the posterior cranial fossa, requiring accurate preoperative diagnosis to avoid accidental intraoperative hemorrhage and even death.
Hemangioblastoma Humans Neural Networks, Computer Retrospective Studies Female Aged Middle Aged Diagnosis, Differential Adolescent Child Brain Stem Neoplasms Child, Preschool Contrast Media Cerebellar Neoplasms Magnetic Resonance Imaging Young Adult Adult Male
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MRI-Based Kinetic Heterogeneity Evaluation in the Accurate Access of Axillary Lymph Node Status in Breast Cancer Using a Hybrid CNN-RNN Model.

Journal of magnetic resonance imaging : JMRI Jan 11, 2024
BACKGROUND: Accurate evaluation of the axillary lymph node (ALN) status is needed for determining the treatment protocol for breast cancer (BC). The value of magnetic resonance imaging (MRI)-based tumor heterogeneity in assessing ALN metastasis in BC...
Humans Neural Networks, Computer Retrospective Studies Adult Breast Neoplasms Middle Aged Female Reproducibility of Results Aged Axilla Kinetics Deep Learning Lymphatic Metastasis ROC Curve Contrast Media Lymph Nodes Magnetic Resonance Imaging
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Morphology-based molecular classification of spinal cord ependymomas using deep neural networks.

Brain pathology (Zurich, Switzerland) Jan 11, 2024
Based on DNA-methylation, ependymomas growing in the spinal cord comprise two major molecular types termed spinal (SP-EPN) and myxopapillary ependymomas (MPE(-A/B)), which differ with respect to their clinical features and prognosis. Due to the exist...
Humans Middle Aged Neural Networks, Computer Deep Learning Female Adolescent Spinal Cord Child, Preschool Aged Young Adult Ependymoma Adult Child Spinal Cord Neoplasms Male DNA Methylation
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State identification for a class of uncertain switched systems by differential neural networks.

Network (Bristol, England) Jan 11, 2024
This paper presents a non-parametric identification scheme for a class of uncertain switched nonlinear systems based on continuous-time neural networks. This scheme is based on a continuous neural network identifier. This adaptive identifier guarante...
Gait Nonlinear Dynamics Uncertainty Humans Neural Networks, Computer Algorithms Computer Simulation
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LPI-SKMSC: Predicting LncRNA-Protein Interactions with Segmented k-mer Frequencies and Multi-space Clustering.

Interdisciplinary sciences, computational life sciences Jan 11, 2024
 Long noncoding RNAs (lncRNAs) have significant regulatory roles in gene expression. Interactions with proteins are one of the ways lncRNAs play their roles. Since experiments to determine lncRNA-protein interactions (LPIs) are expensive and time-con...
RNA, Long Noncoding Proteins Algorithms Computational Biology Cluster Analysis Humans Neural Networks, Computer
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SEA-NET: medical image segmentation network based on spiral squeeze-and-excitation and attention modules.

BMC medical imaging Jan 11, 2024
BACKGROUND: Medical image segmentation is an important processing step in most of medical image analysis. Thus, high accuracy and robustness are required for them. The current deep neural network based medical segmentation methods have good effect on...
Receptor Protein-Tyrosine Kinases Semantics Neuroimaging Entropy Humans Neural Networks, Computer Image Processing, Computer-Assisted
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Designing a use-error robust machine learning model for quantitative analysis of diffuse reflectance spectra.

Journal of biomedical optics Jan 11, 2024
SIGNIFICANCE: Machine learning (ML)-enabled diffuse reflectance spectroscopy (DRS) is increasingly used as an alternative to the computation-intensive inverse Monte Carlo (MCI) simulation to predict tissue's optical properties, including the absorpti...
Spectrum Analysis Monte Carlo Method Phantoms, Imaging Neural Networks, Computer Computer Simulation
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Unsupervised SoftOtsuNet Augmentation for Clinical Dermatology Image Classifiers.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 11, 2024
Data Augmentation is a crucial tool in the Machine Learning (ML) toolbox because it can extract novel, useful training images from an existing dataset, thereby improving accuracy and reducing overfitting in a Deep Neural Network (DNNs). However, clin...
Image Processing, Computer-Assisted Machine Learning Humans Dermatology Algorithms Deep Learning Neural Networks, Computer
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Prediction of Transfusion among In-patient Population using Temporal Pattern based Clinical Similarity Graphs.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 11, 2024
Intelligent prediction of risk of blood transfusion among hospitalized patients can identify at-risk patients and provide timely information to the hospital to plan and reserve resources to meet the demand of blood transfusion. While previously propo...
Vital Signs Humans Neural Networks, Computer Female Blood Transfusion
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A QUEST for Model Assessment: Identifying Difficult Subgroups via Epistemic Uncertainty Quantification.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 11, 2024
Uncertainty quantification in machine learning can provide powerful insight into a model's capabilities and enhance human trust in opaque models. Well-calibrated uncertainty quantification reveals a connection between high uncertainty and an increase...
Machine Learning Humans Benchmarking Neural Networks, Computer Uncertainty Probability
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