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Special Issue "Artificial Intelligence in Radiology 2.0"
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".
Deadline for manuscript submissions: 30 November 2023 | Viewed by 5868
Special Issue Editors
Interests: diagnostic radiology; neuroradiology; machine learning; quantitative modeling
Interests: computer vision; convolutional neural networks; generative adversarial network; self- and semi-supervised learning strategies
Special Issue Information
Advances in computer vision over the past decade have led to a growing interest in machine learning and other artificial intelligence (AI) applications in radiology. While a small but growing number of AI software programs have been approved for clinical use, there are numerous potential uses of AI in radiology that are areas of active investigation. Among the AI processes relevant to radiologic image interpretation is computer-assisted detection or diagnosis, utilizing deep convolutional neural networks and other state-of-the-art AI methodologies to automate such computer vision tasks as image classification, object detection/localization, and image segmentation. Prognostication or clinical decision-making could also be assisted by the AI-facilitated assessment of images and/or other clinical data. There are also potential roles of AI in radiology beyond image interpretation, such as clinical decision support, protocol selection, improving the image acquisition speed or quality, reporting and communication, and other clinical or research workflow processes. We include discussions of the repertoire of available network architectures applicable to radiology, including deep-learning convolutional neural networks commonly employed for image classification and other architectures such as generative adversarial networks and U-Net-based architectures. The aims of this Special Issue are to (1) summarize current research on several broad categories of AI tasks relevant to radiology through a series of multi-disciplinary literature reviews and (2) illustrate AI applications in selected radiology workflows through a diverse set of original research articles.
Dr. Xuan V. Nguyen
Dr. Engin Dikici
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- radiology workflow
- computer-assisted diagnosis
- computer-assisted detection
- machine learning
- deep learning
- convolutional neural networks
- medical diagnosis
- diagnostic radiology