AIMC Topic:
Image Interpretation, Computer-Assisted
Clear Filters Showing 1331 to 1340 of 2720 articles
Clinically Relevant Vulnerabilities of Deep Machine Learning Systems for Skin Cancer Diagnosis.
The Journal of investigative dermatology
Sep 12, 2020
A Comparative Analysis of Two Machine Learning-Based Diagnostic Patterns with Thyroid Imaging Reporting and Data System for Thyroid Nodules: Diagnostic Performance and Unnecessary Biopsy Rate.
Thyroid : official journal of the American Thyroid Association
Sep 9, 2020
The risk stratification system of the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) for thyroid nodules is affected by low diagnostic specificity. Machine learning (ML) methods can optimize the diagnostic perf...
Thyroid Nodule
Predictive Value of Tests
Tumor Burden
Image Interpretation, Computer-Assisted
Adolescent
Thyroid Neoplasms
Ultrasonography
Biopsy, Fine-Needle
Unnecessary Procedures
Elasticity Imaging Techniques
Machine Learning
Retrospective Studies
Adult
Clinical Decision-Making
Reproducibility of Results
Humans
Female
Male
Aged
Aged, 80 and over
Middle Aged
Young Adult
China
Radiomic Model for Distinguishing Dissecting Aneurysms from Complicated Saccular Aneurysms on high-Resolution Magnetic Resonance Imaging.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Sep 8, 2020
OBJECTIVE: To build radiomic model in differentiating dissecting aneurysm (DA) from complicated saccular aneurysm (SA) based on high-resolution magnetic resonance imaging (HR-MRI) through machine-learning algorithm.
Novel and conventional embryo parameters as input data for artificial neural networks: an artificial intelligence model applied for prediction of the implantation potential.
Fertility and sterility
Sep 8, 2020
OBJECTIVE: To describe novel embryo features capable of predicting implantation potential as input data for an artificial neural network (ANN) model.
Female
Time Factors
Pregnancy Rate
Fertilization in Vitro
Neural Networks, Computer
Retrospective Studies
Pregnancy
Fertility
Image Interpretation, Computer-Assisted
Time-Lapse Imaging
Infertility
Treatment Outcome
Embryo Implantation
Predictive Value of Tests
Blastocyst
Humans
Single Embryo Transfer
Oocyte Donation
Synthesis of diagnostic quality cancer pathology images by generative adversarial networks.
The Journal of pathology
Sep 8, 2020
Deep learning-based computer vision methods have recently made remarkable breakthroughs in the analysis and classification of cancer pathology images. However, there has been relatively little investigation of the utility of deep neural networks to s...
Deep learning with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping.
Artificial intelligence in medicine
Sep 7, 2020
Cardiac magnetic resonance quantitative T1-mapping is increasingly used for advanced myocardial tissue characterisation. However, cardiac or respiratory motion can significantly affect the diagnostic utility of T1-maps, and thus motion artefact detec...
Computational Radiology in Breast Cancer Screening and Diagnosis Using Artificial Intelligence.
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Aug 31, 2020
Breast cancer screening has been shown to significantly reduce mortality in women. The increased utilization of screening examinations has led to growing demands for rapid and accurate diagnostic reporting. In modern breast imaging centers, full-fiel...
Machine learning-based radiomics analysis in predicting the meningioma grade using multiparametric MRI.
European journal of radiology
Aug 28, 2020
PURPOSE: To investigate the prediction performance of radiomic models based on multiparametric MRI in predicting the meningioma grade.
HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy.
Scientific data
Aug 28, 2020
Artificial intelligence is currently a hot topic in medicine. However, medical data is often sparse and hard to obtain due to legal restrictions and lack of medical personnel for the cumbersome and tedious process to manually label training data. The...
Development of Convolutional Neural Networks to identify bone metastasis for prostate cancer patients in bone scintigraphy.
Annals of nuclear medicine
Aug 24, 2020
OBJECTIVE: The main aim of this work is to build a robust Convolutional Neural Network (CNN) algorithm that efficiently and quickly classifies bone scintigraphy images, by determining the presence or absence of prostate cancer metastasis.
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