Special Issue "Deep Learning in Cancer Imaging: Developments and Future Prospects"

A special issue of Tomography (ISSN 2379-139X). This special issue belongs to the section "Cancer Imaging".

Deadline for manuscript submissions: 28 February 2023 | Viewed by 145

Special Issue Editor

Dr. Frezghi Habte
E-Mail Website
Guest Editor
Stanford Center for Innovation in In Vivo Imaging, Stanford School of Medicine, Stanford, CA 94304, USA
Interests: radiology; quantitative molecular imaging; medical imaging; mathematical modeling

Special Issue Information

Dear Colleagues,

Machine learning, a subbranch of artificial intelligence, is a technique used to recognize patterns from trained data. Machine learning has been applied in medical imaging, but the recent advancements in deep learning have gained the attention of experts both in academia and industry. As a subfield of machine learning, deep learning methods are now widespread in a variety of businesses and institutes such as health care worldwide. Likewise, the application of deep learning methods in cancer imaging is also accelerating, with the center of the application being cancer diagnosis, involving the development of models for automated analysis to achieve expert-level performance in routine clinical diagnostic tasks. Deep learning is also used to harness new knowledge by uncovering hidden patterns in data for better diagnosis, prognosis, and treatment responses.

Thus, this Special Issue will highlight the recent developments in and prospects of machine/deep learning methods and applications in cancer imaging. This includes the development and implementation of algorithms, data science, and new data management tools, the preclinical and clinical advancement of machine/deep learning, reviews, trends, and future aspects of machine/deep learning in cancer imaging.

Dr. Frezghi Habte
Guest Editor

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 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 1800 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

  • deep learning
  • machine learning
  • medical imaging
  • cancer
  • imaging data science
  • image analysis
  • classification
  • segmentation
  • predication

Published Papers

This special issue is now open for submission.
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