Deep Learning Strategies for Tomography
A special issue of Tomography (ISSN 2379-139X).
Deadline for manuscript submissions: closed (30 October 2022) | Viewed by 333
Special Issue Editors
Interests: medical tomography; computer vision and pattern recognition; artificial intelligence in medicine brain computer interfaces
Special Issue Information
Dear Colleagues,
Tomography is fundamental in medicine, both for diagnosis and, in recent years, for treatment. It is performed in a single modality or by combining different modalities in order to obtain augmented reality (more information than that contained in each of the mixed modalities). This has led to an exponential increase in the number of data points produced, which need to be numerically processed and analyzed in order to ensure fast and objective measurements/evaluations. Deep learning has significantly revolutionized data/driven acquisition strategies and automated processing/analysis/interpretation. In fact, several tasks, including (but not limited to) sampling and reconstruction, filtering, compression, processing, registration, fusion, segmentation, abnormality detection and quantification, localization and interpretation, are benefiting from the new paradigms made available by deep learning.
In addition, deep learning provides a unique tool for allowing investigators the opportunity to more precisely define and justify integrated findings related to the diverse and heterogeneous features associated with underlying anatomical, physiological, functional, metabolic, and molecular genetic activities of normal and diseased tissue. This Special Issue aims to explore deep learning strategies in every aspect of “quantitative” tomography.
Dr. Giuseppe Placidi
Prof. Luigi Cinque
Guest Editors
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. Tomography is an international peer-reviewed open access monthly 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 2400 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.
Keywords
- medical tomography
- deep learning
- medical imaging
- computer vision and pattern recognition
- artificial intelligence in medicine
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