Contrast Media & Molecular Imaging

Artificial Intelligence in Radiomics


Publishing date
01 Jan 2022
Status
Closed
Submission deadline
27 Aug 2021

Lead Editor

1University of Leicester, Leicester, UK

2University of Granada, Granada, Spain

3Vanderbilt University, Vanderbilt, USA

This issue is now closed for submissions.
More articles will be published in the near future.

Artificial Intelligence in Radiomics

This issue is now closed for submissions.
More articles will be published in the near future.

Description

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)

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 7830909
  • - Research Article

Predicting the Grade of Prostate Cancer Based on a Biparametric MRI Radiomics Signature

Li Zhang | Xia Zhe | ... | Longchao Li
  • Special Issue
  • - Volume 2021
  • - Article ID 2015780
  • - Research Article

Deep Learning in the Classification of Stage of Liver Fibrosis in Chronic Hepatitis B with Magnetic Resonance ADC Images

Ziquan Zhu | Daoyan Lv | ... | Guijuan Zhu
  • Special Issue
  • - Volume 2021
  • - Article ID 6044256
  • - Research Article

Drug-Target Interaction Prediction Based on Multisource Information Weighted Fusion

Shuaiqi Liu | Jingjie An | ... | ShuiHua Wang
  • Special Issue
  • - Volume 2021
  • - Article ID 3257035
  • - Review Article

AIoT Used for COVID-19 Pandemic Prevention and Control

Shu-Wen Chen | Xiao-Wei Gu | ... | Hui-Sheng Zhu
  • Special Issue
  • - Volume 2021
  • - Article ID 7192016
  • - Research Article

Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories

Omneya Attallah | Maha Sharkas
  • Special Issue
  • - Volume 2021
  • - Article ID 6890024
  • - Research Article

Integration and Segregation of Dynamic Functional Connectivity States for Mild Cognitive Impairment Revealed by Graph Theory Indicators

Zhuqing Jiao | Peng Gao | ... | Haifeng Shi
Contrast Media & Molecular Imaging
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Acceptance rate49%
Submission to final decision47 days
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CiteScore4.900
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Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.