Artificial Intelligence Techniques for Medical Image Processing
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (25 February 2024)
Special Issue Editors
Interests: artificial intelligent on medical physics and medical informatics; radiation oncology; radiation biophysical modeling; evolutionary optimization
Special Issues, Collections and Topics in MDPI journals
Interests: biomedical engineering; artificial intelligence; medical informatics; radiation therapy application
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; deep learning; artificial intelligence health; medical informatics; information security
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
In recent years, artificial intelligence (AI) technology has made remarkable progress in medical image processing and applications and has provided many opportunities for improving the quality of related biomedical research. However, there are still many foreseeable challenges such as system performance, clinical application, data labeling and quality analysis, etc. The goal of this Special Issue is to invite interested parties to publish original manuscripts presenting the latest research results of artificial intelligence-related technologies applied to medical imaging. By analyzing medical images and signals through artificial intelligence, it will become feasible to use machine learning or deep learning algorithms to predict clinical diagnosis or prognosis accurately. We welcome submissions of various applications of biomedical images, signals, and data combined with state-of-the-art machine or deep learning, as well as proposing innovative approaches to address existing imaging/data challenges in biomedicine and health.
Topics of interest include but are not limited to:
- Machine learning or deep learning algorithms for medical image processing applications;
- Disease classification and prognosis prediction based on medical images, signals, and data processing;
- Segmentation and anomaly detection based on medical images;
- Medical image denoising and quality improvement;
- Medical image, signals, and data processing for therapy monitoring;
- Big data or multimodal data processing for predicting clinical outcomes;
- Medical image quality assessment;• Disease severity assessment based on medical images, signals and data processing.
Dr. Jack Yang
Prof. Dr. Tsair-Fwu Lee
Dr. Chih-Hsueh Lin
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. Applied Sciences 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 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
- artificial intelligence
- machine learning
- deep learning
- medical image processing
- Medical image denoising
