AIMC Topic: False Positive Reactions

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Identifying synonymy between relational phrases using word embeddings.

Journal of biomedical informatics May 22, 2015
Many text mining applications in the biomedical domain benefit from automatic clustering of relational phrases into synonymous groups, since it alleviates the problem of spurious mismatches caused by the diversity of natural language expressions. Mos...
Fuzzy Logic Semantics Cluster Analysis MEDLINE Models, Statistical False Positive Reactions Natural Language Processing Vocabulary, Controlled Algorithms Data Mining Databases, Factual Probability Reproducibility of Results Markov Chains Medical Informatics
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Multi-scale textural feature extraction and particle swarm optimization based model selection for false positive reduction in mammography.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Feb 24, 2015
The high number of false positives and the resulting number of avoidable breast biopsies are the major problems faced by current mammography Computer Aided Detection (CAD) systems. False positive reduction is not only a requirement for mass but also ...
Models, Statistical Humans Reproducibility of Results False Positive Reactions Mammography Sensitivity and Specificity Radiographic Image Enhancement Algorithms Radiographic Image Interpretation, Computer-Assisted Breast Neoplasms Computer Simulation Pattern Recognition, Automated Support Vector Machine Female
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Quantifying the determinants of outbreak detection performance through simulation and machine learning.

Journal of biomedical informatics Nov 6, 2014
OBJECTIVE: To develop a probabilistic model for discovering and quantifying determinants of outbreak detection and to use the model to predict detection performance for new outbreaks.
ROC Curve Computational Biology Bayes Theorem Probability Algorithms Computer Simulation Humans Sensitivity and Specificity False Positive Reactions Disease Outbreaks Machine Learning
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A Generalized Machine Learning Model for Identifying Congenital Heart Defects (CHDs) Using ICD Codes.

Birth defects research Feb 1, 2025
BACKGROUND: International Classification of Diseases (ICD) codes utilized for congenital heart defect (CHD) case identification in datasets have substantial false-positive (FP) rates. Incorporating machine learning (ML) algorithms following case sele...
Female Male False Positive Reactions International Classification of Diseases Humans Heart Defects, Congenital Machine Learning Algorithms
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A semi-supervised deep neuro-fuzzy iterative learning system for automatic segmentation of hippocampus brain MRI.

Mathematical biosciences and engineering : MBE Dec 11, 2024
The hippocampus is a small, yet intricate seahorse-shaped tiny structure located deep within the brain's medial temporal lobe. It is a crucial component of the limbic system, which is responsible for regulating emotions, memory, and spatial navigatio...
Magnetic Resonance Imaging Fuzzy Logic Supervised Machine Learning Hippocampus Humans Algorithms False Positive Reactions Neural Networks, Computer Brain Deep Learning Image Processing, Computer-Assisted
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A Semiautonomous Deep Learning System to Reduce False Positives in Screening Mammography.

Radiology. Artificial intelligence May 1, 2024
Purpose To evaluate the ability of a semiautonomous artificial intelligence (AI) model to identify screening mammograms not suspicious for breast cancer and reduce the number of false-positive examinations. Materials and Methods The deep learning alg...
Deep Learning United States Early Detection of Cancer Radiographic Image Interpretation, Computer-Assisted Humans Aged Mammography False Positive Reactions Retrospective Studies Adult Female Breast Neoplasms Middle Aged
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The diagnostic performance of deep-learning-based CT severity score to identify COVID-19 pneumonia.

The British journal of radiology Jan 1, 2022
OBJECTIVE: To determine the diagnostic accuracy of a deep-learning (DL)-based algorithm using chest computed tomography (CT) scans for the rapid diagnosis of coronavirus disease 2019 (COVID-19), as compared to the reference standard reverse-transcrip...
Tomography, X-Ray Computed Female Reproducibility of Results Aged Humans COVID-19 Retrospective Studies Deep Learning Image Processing, Computer-Assisted False Negative Reactions False Positive Reactions Adult Sensitivity and Specificity Male Severity of Illness Index Radiography, Thoracic
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DeepCNV: a deep learning approach for authenticating copy number variations.

Briefings in bioinformatics Sep 2, 2021
Copy number variations (CNVs) are an important class of variations contributing to the pathogenesis of many disease phenotypes. Detecting CNVs from genomic data remains difficult, and the most currently applied methods suffer from an unacceptably hig...
Disease Deep Learning ROC Curve DNA Copy Number Variations Area Under Curve Benchmarking Datasets as Topic Genome, Human False Positive Reactions Humans
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Deep Learning Models for Gastric Signet Ring Cell Carcinoma Classification in Whole Slide Images.

Technology in cancer research & treatment Jan 1, 2021
Signet ring cell carcinoma (SRCC) of the stomach is a rare type of cancer with a slowly rising incidence. It tends to be more difficult to detect by pathologists, mainly due to its cellular morphology and diffuse invasion manner, and it has poor prog...
Supervised Machine Learning False Negative Reactions False Positive Reactions ROC Curve Computational Biology Area Under Curve Carcinoma, Signet Ring Cell Humans Stomach Neoplasms Deep Learning
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Alexa, do I have COVID-19?

Nature Oct 1, 2020
Humans Machine Learning Pandemics COVID-19 Biomarkers Autism Spectrum Disorder Pulmonary Disease, Chronic Obstructive Depression Speech Recognition Software Triage Pneumonia, Viral False Positive Reactions Dementia Privacy Voice Coronavirus Infections Mobile Applications Speech Disorders Dyspnea Mass Screening Parkinson Disease
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