AIMC Topic: Diagnostic Imaging

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Challenges and opportunities for artificial intelligence in oncological imaging.

Clinical radiology Apr 24, 2021
Imaging plays a key role in oncology, including the diagnosis and detection of cancer, determining clinical management, assessing treatment response, and complications of treatment or disease. The current use of clinical oncology is predominantly qua...
Image Interpretation, Computer-Assisted Medical Oncology Neoplasms Diagnostic Imaging Precision Medicine Humans Artificial Intelligence
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Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis.

European radiology Apr 16, 2021
OBJECTIVES: To perform a systematic review of design and reporting of imaging studies applying convolutional neural network models for radiological cancer diagnosis.
Research Design Neoplasms Diagnostic Imaging Neural Networks, Computer Artificial Intelligence Humans
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Data valuation for medical imaging using Shapley value and application to a large-scale chest X-ray dataset.

Scientific reports Apr 16, 2021
The reliability of machine learning models can be compromised when trained on low quality data. Many large-scale medical imaging datasets contain low quality labels extracted from sources such as medical reports. Moreover, images within a dataset may...
Algorithms Image Processing, Computer-Assisted Machine Learning Datasets as Topic Radiography, Thoracic Pneumonia Diagnostic Imaging Humans Neural Networks, Computer
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Artificial intelligence in ultrasound.

European journal of radiology Apr 12, 2021
Ultrasound (US), a flexible green imaging modality, is expanding globally as a first-line imaging technique in various clinical fields following with the continual emergence of advanced ultrasonic technologies and the well-established US-based digital ...
Humans Ultrasonography Machine Learning Diagnostic Imaging Artificial Intelligence Diagnosis, Computer-Assisted
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Multivariate analysis of Brillouin imaging data by supervised and unsupervised learning.

Journal of biophotonics Apr 4, 2021
Brillouin imaging relies on the reliable extraction of subtle spectral information from hyperspectral datasets. To date, the mainstream practice has been to use line fitting of spectral features to retrieve the average peak shift and linewidth parame...
Diagnostic Imaging Multivariate Analysis Image Processing, Computer-Assisted Unsupervised Machine Learning Algorithms
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Basic of machine learning and deep learning in imaging for medical physicists.

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) Apr 4, 2021
The manuscript aims at providing an overview of the published algorithms/automation tool for artificial intelligence applied to imaging for Healthcare. A PubMed search was performed using the query string to identify the proposed approaches (algorith...
Artificial Intelligence Deep Learning Diagnostic Imaging Machine Learning Algorithms
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Text-Guided Neural Network Training for Image Recognition in Natural Scenes and Medicine.

IEEE transactions on pattern analysis and machine intelligence Apr 1, 2021
Convolutional neural networks (CNNs) are widely recognized as the foundation for machine vision systems. The conventional rule of teaching CNNs to understand images requires training images with human annotated labels, without any additional instruct...
Algorithms Diagnostic Imaging Humans Neural Networks, Computer
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PyDiNet: Pyramid Dilated Network for medical image segmentation.

Neural networks : the official journal of the International Neural Network Society Mar 26, 2021
Medical image segmentation is an important step in many generic applications such as population analysis and, more accessible, can be made into a crucial tool in diagnosis and treatment planning. Previous approaches are based on two main architecture...
Diagnostic Imaging Image Processing, Computer-Assisted Humans Neural Networks, Computer Software
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Active, continual fine tuning of convolutional neural networks for reducing annotation efforts.

Medical image analysis Mar 24, 2021
The splendid success of convolutional neural networks (CNNs) in computer vision is largely attributable to the availability of massive annotated datasets, such as ImageNet and Places. However, in medical imaging, it is challenging to create such larg...
Diagnostic Imaging Humans Neural Networks, Computer Longitudinal Studies
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Reply to Higaki-Next Steps in Artificial Intelligence for Cardiovascular Hemodynamics.

The Canadian journal of cardiology Mar 13, 2021
Hemodynamics Diagnostic Imaging Cardiovascular System Artificial Intelligence Humans Algorithms
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