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AIR-CMMI 2021 : Artificial Intelligence in Radiomics - Contrast Media & Molecular Imaging 2021

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Link: https://www.hindawi.com/journals/cmmi/si/125613/
 
When N/A
Where N/A
Abstract Registration Due Aug 1, 2021
Submission Deadline Aug 27, 2021
Notification Due Oct 15, 2021
Final Version Due Nov 15, 2021
Categories    artificial intelligence   radiomics   deep learning
 

Call For Papers

Radiomics is a method that extracts many features from radiographic medical images using data characterization algorithms. Recently, the applications of Artificial Intelligence (AI) are of increasing use in radiographic medical images from various sources: computed tomography (CT), photon-counting CT, spectral photon-counting CT, ultrasound contrast agents, magnetic resonance imaging (MRI), positron emission tomography (PET), mammography, thermography, PET, magnetic resonance spectroscopy imaging (MRSI), etc. AI is leading to a significant evolution of automatic diagnosis systems supporting researchers and users. The paradigms of AI allow humans to create machines capable of reasoning, perceiving reality, learning from radiographic medical images, identifying models, grouping data and information.

This Special Issue aims to provide a forum to update and discuss new discoveries, challenges, opportunities, methods, and specific applications regarding the use of AI in radiomics. Both original research and review articles are welcome. Studies should focus on major trends and challenges in this field.

Potential topics include but are not limited to the following:

Contrast media in radiomics
Radiomic image analysis
AI techniques in radiomics: machine (deep) learning, transfer learning, attention neural network, graph neural network
Big-data methods in radiomics
AI techniques in genomics and molecular imaging
Clinical studies via radiomics
Artificial intelligence used in radiomic applications (e.g. brain imaging, breast cancer imaging, cardiography, etc.)
Automatic procedures for medical assessment (segmentation of linear and nonlinear structures, image registration, volume of interest (VOI) selection, quantification and analysis of physiological measures)

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