AIMC Topic: Humans

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Automatic classification of ultrasound breast lesions using a deep convolutional neural network mimicking human decision-making.

European radiology Mar 29, 2019
OBJECTIVES: To evaluate a deep convolutional neural network (dCNN) for detection, highlighting, and classification of ultrasound (US) breast lesions mimicking human decision-making according to the Breast Imaging Reporting and Data System (BI-RADS).
Observer Variation ROC Curve Ultrasonography, Mammary Image Interpretation, Computer-Assisted Clinical Decision-Making Female Aged Adult Breast Neoplasms Middle Aged Neural Networks, Computer Algorithms Retrospective Studies Humans
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Relative importance of symptoms, cognition, and other multilevel variables for psychiatric disease classifications by machine learning.

Psychiatry research Mar 29, 2019
This study used machine-learning algorithms to make unbiased estimates of the relative importance of various multilevel data for classifying cases with schizophrenia (n = 60), schizoaffective disorder (n = 19), bipolar disorder (n = 20), unipolar dep...
Depressive Disorder, Major Bipolar Disorder Parents Schizophrenic Psychology Cognition Schizophrenia Adult Humans Psychotic Disorders Male Machine Learning Middle Aged Female
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Artificial Intelligence in Nuclear Cardiology.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine Mar 29, 2019
Cardiology Nuclear Medicine Diagnostic Imaging Tomography, Emission-Computed, Single-Photon Myocardial Perfusion Imaging Humans Algorithms Deep Learning Artificial Intelligence Image Processing, Computer-Assisted Prognosis Machine Learning Multicenter Studies as Topic Risk Assessment
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DeepQSM - using deep learning to solve the dipole inversion for quantitative susceptibility mapping.

NeuroImage Mar 29, 2019
Quantitative susceptibility mapping (QSM) is based on magnetic resonance imaging (MRI) phase measurements and has gained broad interest because it yields relevant information on biological tissue properties, predominantly myelin, iron and calcium in ...
Humans Algorithms Brain Male Female Magnetic Resonance Imaging Deep Learning Young Adult Brain Mapping Image Processing, Computer-Assisted Adult
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WHO and ITU establish benchmarking process for artificial intelligence in health.

Lancet (London, England) Mar 29, 2019
World Health Organization Congresses as Topic Humans Artificial Intelligence Global Health Benchmarking
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Practical implementation of artificial intelligence algorithms in pulmonary auscultation examination.

European journal of pediatrics Mar 29, 2019
Lung auscultation is an important part of a physical examination. However, its biggest drawback is its subjectivity. The results depend on the experience and ability of the doctor to perceive and distinguish pathologies in sounds heard via a stethosc...
Humans Neural Networks, Computer Adolescent Child, Preschool Child Machine Learning Algorithms Infant Stethoscopes Auscultation Respiratory Sounds
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Bagging and deep learning in optimal individualized treatment rules.

Biometrics Mar 29, 2019
An ENsemble Deep Learning Optimal Treatment (EndLot) approach is proposed for personalized medicine problems. The statistical framework of the proposed method is based on the outcome weighted learning (OWL) framework which transforms the optimal deci...
Humans Deep Learning Cell Line, Tumor Decision Support Systems, Clinical Databases as Topic Neural Networks, Computer Computer Simulation Precision Medicine
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Binary tree-like network with two-path Fusion Attention Feature for cervical cell nucleus segmentation.

Computers in biology and medicine Mar 29, 2019
Early detection of cervical lesion is of great significance in reducing mortality from cervical cancer, and segmentation of cervical cell nuclei plays an important role in screening for cervical lesion. Compared with traditional algorithms, several d...
Uterine Cervical Neoplasms Deep Learning Image Processing, Computer-Assisted Humans Female Cell Nucleus
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Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis.

Medical image analysis Mar 29, 2019
Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. While medical imaging datasets have been growing in size, a challenge for supervised ML algorithms that is frequently mentioned is the lack of annotated d...
Image Processing, Computer-Assisted Diagnostic Imaging Supervised Machine Learning Humans
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Analysis of a CT patient dose database with an unsupervised clustering approach.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) Mar 29, 2019
PURPOSE: This study investigated the benefits of implementing a cluster analysis technique to extract relevant information from a computed tomography (CT) dose registry archive.
Radiation Dosage Databases, Factual Phantoms, Imaging Tomography, X-Ray Computed Cluster Analysis Unsupervised Machine Learning Humans Registries Software Data Mining
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