Medical Imaging and Artificial Intelligence in Cancer
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".
Deadline for manuscript submissions: 31 March 2025 | Viewed by 291
Special Issue Editors
Interests: medical imaging; magnetic resonance imaging; cancer; artificial intelligence analysis; computed tomography; cardiovascular imaging
Interests: pancreatic cellular physiology; mechanism of pancreatic diseases; acute pancreatitis; chronic pancreatitis; pancreatic cancer; pediatric pancreatitis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Medical imaging is a critical part of cancer management and research. Despite all the advances in cancer imaging, early detection, accurate diagnosis, and tumor characterization with medical imaging remain challenging due to limitations in image spatial resolution, sensitivity, image quality variability, and image interpretation. Artificial intelligence (AI) in image acquisition, reconstruction, analysis, and diagnosis have revolutionized cancer imaging in recent years. This allows for accelerated data acquisition, enhanced signal-to-noise ratio and contrast, precise cancer prediction, diagnosis, therapeutic guidance, and outcome prediction. Major challenges related to AI in medical imaging include data availability, quality and uniformity, interpretability, bias and generalization, and legal and ethical concerns. Addressing these challenges is crucial for improving cancer diagnosis and treatment.
For this Special Issue, we solicit manuscripts on medical imaging and artificial intelligence to address important technical and clinical questions related to cancer. Topics include but are not limited to the technical development and clinical application of medical imaging and AI for cancer risk stratification, early diagnosis, treatment response prediction and assessment, and image-guided intervention. We hope that this Special Issue will introduce state-of-the art imaging and AI to the cancer research community.
Debiao Li, PhD
Director, Biomedical Imaging Research Institute
Karl Storz Chair, Minimally Invasive Surgery In Honor of Dr. George Berci
Professor, Biomedical Sciences and Imaging
Cedars-Sinai Medical Center
Professor, Medicine and Bioengineering
University of California, Los Angeles
Office: 310 423 7743
https://www.cedars-sinai.edu/research/labs/li.html
https://www.cedars-sinai.edu/research/departments-institutes/biomedical-imaging.html
Prof. Dr. Debiao Li
Prof. Dr. Stephen J. Pandol
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 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. Cancers 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 2900 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
- cancer
- medical imaging
- magnetic resonance imaging
- computed tomography
- artificial intelligence
- image analysis
- cancer detection
- cancer prediction
- cancer characterization
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