AIMC Topic: False Positive Reactions

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The effects of physics-based data augmentation on the generalizability of deep neural networks: Demonstration on nodule false-positive reduction.

Medical physics Aug 27, 2019
PURPOSE: An important challenge for deep learning models is generalizing to new datasets that may be acquired with acquisition protocols different from the training set. It is not always feasible to expand training data to the range encountered in cl...
Radiation Dosage Normal Distribution Deep Learning Image Processing, Computer-Assisted Humans Lung Neoplasms Tomography, X-Ray Computed False Positive Reactions Sensitivity and Specificity
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A Lightweight API-Based Approach for Building Flexible Clinical NLP Systems.

Journal of healthcare engineering Aug 15, 2019
Natural language processing (NLP) has become essential for secondary use of clinical data. Over the last two decades, many clinical NLP systems were developed in both academia and industry. However, nearly all existing systems are restricted to speci...
Data Mining Medical Informatics Clinical Trials as Topic False Positive Reactions Obesity Humans Natural Language Processing Machine Learning Pharmaceutical Preparations Databases, Factual Reproducibility of Results Patient Discharge
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Automatic diagnosis of fungal keratitis using data augmentation and image fusion with deep convolutional neural network.

Computer methods and programs in biomedicine Aug 9, 2019
BACKGROUND AND OBJECTIVES: Fungal keratitis is caused by inflammation of the cornea that results from infection by fungal organisms. The lack of an early effective diagnosis often results in serious complications even blindness. Confocal microscopy i...
Deep Learning Image Processing, Computer-Assisted Microscopy, Confocal Mycoses Reproducibility of Results Cornea Pattern Recognition, Automated Keratitis False Positive Reactions Diagnosis, Computer-Assisted Humans Neural Networks, Computer Algorithms Databases, Factual
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Comparison of text processing methods in social media-based signal detection.

Pharmacoepidemiology and drug safety Aug 7, 2019
PURPOSE: Adverse event (AE) identification in social media (SM) can be performed using various types of natural language processing (NLP) and machine learning (ML). These methods can be categorized by complexity and precision level. Co-occurrence-bas...
Retrospective Studies Sensitivity and Specificity Social Media Pharmacovigilance Datasets as Topic False Negative Reactions False Positive Reactions Adverse Drug Reaction Reporting Systems Natural Language Processing Feasibility Studies Drug-Related Side Effects and Adverse Reactions Data Collection
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On the interpretability of machine learning-based model for predicting hypertension.

BMC medical informatics and decision making Jul 29, 2019
BACKGROUND: Although complex machine learning models are commonly outperforming the traditional simple interpretable models, clinicians find it hard to understand and trust these complex models due to the lack of intuition and explanation of their pr...
Datasets as Topic Cardiorespiratory Fitness False Positive Reactions Risk Factors Male Humans Hypertension Machine Learning Female Exercise Test
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Development and validation of a deep-learning algorithm for the detection of neovascular age-related macular degeneration from colour fundus photographs.

Clinical & experimental ophthalmology Jul 25, 2019
IMPORTANCE: Detection of early onset neovascular age-related macular degeneration (AMD) is critical to protecting vision.
Diagnosis, Computer-Assisted Deep Learning ROC Curve Visual Acuity Reproducibility of Results Sensitivity and Specificity Male Middle Aged Fluorescein Angiography Wet Macular Degeneration Photography Ophthalmologists Choroidal Neovascularization Predictive Value of Tests Area Under Curve Fundus Oculi False Positive Reactions Algorithms Retrospective Studies Female Humans
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A CAD system for pulmonary nodule prediction based on deep three-dimensional convolutional neural networks and ensemble learning.

PloS one Jul 12, 2019
BACKGROUND: Detection of pulmonary nodules is an important aspect of an automatic detection system. Incomputer-aided diagnosis (CAD) systems, the ability to detect pulmonary nodules is highly important, which plays an important role in the diagnosis ...
Radiographic Image Interpretation, Computer-Assisted False Positive Reactions Reference Standards Tomography, X-Ray Computed Lung Neoplasms Humans Diagnosis, Computer-Assisted Multiple Pulmonary Nodules Neural Networks, Computer Imaging, Three-Dimensional Solitary Pulmonary Nodule Early Detection of Cancer
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AMC-Net: Asymmetric and multi-scale convolutional neural network for multi-label HPA classification.

Computer methods and programs in biomedicine Jul 9, 2019
BACKGROUND AND OBJECTIVES: The multi-label Human Protein Atlas (HPA) classification can yield a better understanding of human diseases and help doctors to enhance the automatic analysis of biomedical images. The existing automatic protein recognition...
Image Processing, Computer-Assisted Proteins Databases, Protein Microscopy, Fluorescence Microtubules Pattern Recognition, Automated Cell Nucleus Microscopy Probability Neural Networks, Computer Algorithms Phenotype Reproducibility of Results Humans False Positive Reactions
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Evaluation of an AI system for the automated detection of glaucoma from stereoscopic optic disc photographs: the European Optic Disc Assessment Study.

Eye (London, England) Jul 2, 2019
OBJECTIVES: To evaluate the performance of a deep learning based Artificial Intelligence (AI) software for detection of glaucoma from stereoscopic optic disc photographs, and to compare this performance to the performance of a large cohort of ophthal...
False Positive Reactions Photography Ophthalmologists Glaucoma, Open-Angle Optometrists Predictive Value of Tests Observer Variation Optic Disk Optic Nerve Diseases Deep Learning Clinical Competence ROC Curve Europe Humans Artificial Intelligence Reproducibility of Results Retrospective Studies Sensitivity and Specificity Diagnosis, Computer-Assisted
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Scale-space approximated convolutional neural networks for retinal vessel segmentation.

Computer methods and programs in biomedicine Jun 29, 2019
BACKGROUND AND OBJECTIVE: Retinal fundus images are widely used to diagnose retinal diseases and can potentially be used for early diagnosis and prevention of chronic vascular diseases and diabetes. While various automatic retinal vessel segmentation...
Retinal Diseases Normal Distribution Retinal Vessels Image Processing, Computer-Assisted Signal Processing, Computer-Assisted Humans Neural Networks, Computer Area Under Curve Fundus Oculi Algorithms Deep Learning False Positive Reactions ROC Curve
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