AIMC Topic: Diagnostic Imaging

Clear Filters Showing 581 to 590 of 978 articles

Detection of Rare Objects by Flow Cytometry: Imaging, Cell Sorting, and Deep Learning Approaches.

International journal of molecular sciences Mar 27, 2020
Flow cytometry nowadays is among the main working instruments in modern biology paving the way for clinics to provide early, quick, and reliable diagnostics of many blood-related diseases. The major problem for clinical applications is the detection ...
Automation Hematologic Diseases Flow Cytometry Diagnostic Tests, Routine Rare Diseases Cell Tracking Deep Learning Blood Circulation Staining and Labeling Magnetics Cell Separation Neoplastic Cells, Circulating Fluorescent Dyes Diagnostic Imaging Humans
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Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies.

BMJ (Clinical research ed.) Mar 25, 2020
OBJECTIVE: To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians.
Bias Physicians Deep Learning Randomized Controlled Trials as Topic Diagnostic Imaging Image Processing, Computer-Assisted Algorithms Research Design Humans
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Ethics of Using and Sharing Clinical Imaging Data for Artificial Intelligence: A Proposed Framework.

Radiology Mar 24, 2020
In this article, the authors propose an ethical framework for using and sharing clinical data for the development of artificial intelligence (AI) applications. The philosophical premise is as follows: when clinical data are used to provide care, the ...
Ethics, Medical Information Dissemination Artificial Intelligence Diagnostic Imaging Humans
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Artificial intelligence, diagnostic imaging and neglected tropical diseases: ethical implications.

Bulletin of the World Health Organization Mar 3, 2020
Humans Artificial Intelligence Tropical Medicine Diagnostic Imaging Neglected Diseases
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An introduction to deep learning in medical physics: advantages, potential, and challenges.

Physics in medicine and biology Mar 3, 2020
As one of the most popular approaches in artificial intelligence, deep learning (DL) has attracted a lot of attention in the medical physics field over the past few years. The goals of this topical review article are twofold. First, we will provide a...
Physics Deep Learning Diagnostic Imaging Humans
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Radiomics: from qualitative to quantitative imaging.

The British journal of radiology Feb 26, 2020
Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and...
Workflow Technology, Radiologic Brain Neoplasms Lung Neoplasms Male Radiography Humans Deep Learning Female Diagnostic Imaging Forecasting Image Processing, Computer-Assisted Breast Neoplasms
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Preparing Medical Imaging Data for Machine Learning.

Radiology Feb 18, 2020
Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief...
Machine Learning Algorithms Humans Data Collection Data Management Diagnostic Imaging
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[Methods of artificial intelligence and their application in imaging diagnostics].

Magyar onkologia Feb 4, 2020
Artificial intelligence is a dynamically evolving methodology and, due to its large number of methods, its appearance becomes more important not only in industry but also in all disciplines. Diagnostic instrument manufacturers have realized relativel...
Diagnostic Imaging Machine Learning Quality Improvement Humans Algorithms Artificial Intelligence
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Unsupervised Domain Adaptation to Classify Medical Images Using Zero-Bias Convolutional Auto-Encoders and Context-Based Feature Augmentation.

IEEE transactions on medical imaging Feb 3, 2020
The accuracy and robustness of image classification with supervised deep learning are dependent on the availability of large-scale labelled training data. In medical imaging, these large labelled datasets are sparse, mainly related to the complexity ...
Neural Networks, Computer Diagnostic Imaging
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Real-world applications of deep convolutional neural networks in diagnostic cancer imaging.

Chinese clinical oncology Feb 1, 2020
Artificial Intelligence Humans Diagnostic Imaging Neoplasms Algorithms Neural Networks, Computer
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