Advances in Signal and Image Processing in Biomedical Applications
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".
Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 279
Special Issue Editors
Interests: time-frequency analysis; biomedical signal processing; computer-aided diagnosis
Interests: deep learning; biomeical image processing; computer-aided diagnosis and treatment
Interests: computer vision; information security; computer-aided diagnosis
Special Issue Information
Dear Colleagues,
Biomedical signals and images are key observations for elaborating the diagnosis, for assisting the delivery of the treatment and for the follow-up of the patient. In the past decade, there have been many remarkable achievements in biomedical signal and image processing based on machine learning. Many researchers integrate traditional signal and image processing approaches (time-frequency analysis methods, wavelets, principal component analysis, etc.) with machine learning methods to better deal with biomedical signal- and image-processing-related tasks. This Special Issue is dedicated to advanced methods of signal and image processing and their wide application in the field of the biomedical domain and will focus on original research papers and comprehensive review papers related to the methods and applications of novel biomedical signal and image processing. Topics of interest in this Special Issue include, but are not limited to, the following:
1. Application of machine learning methods in biomedical signal and image processing (convolutional neural networks, recurrent neural networks, transformers, graph neural networks, multimodal deep learning networks, diffusion models, etc.);2. Recent advances in biomedical signal processing (signal acquisition, signal separation, signal denoising, model-based processing, signal classification, signal generation, etc.);
3. Recent advances in biomedical image processing (image acquisition, image denoising, image classification, image segmentation, image super-resolution restoration, image enhancement, image registration, image cross-modal generation, image report generation, etc.).
- advanced biomedical signal processing;
- advanced biomedical image processing;
- time-frequency analysis;
- deep learning;
- graph neural network;
- multimodal network;
- diffusion model.
Prof. Dr. Lotfi Senhadji
Prof. Dr. Guanyu Yang
Dr. Zhuhong Shao
Dr. Jiasong Wu
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
- advanced biomedical signal processing
- advanced biomedical image processing
- time-frequency analysis
- deep learning
- graph neural network
- multimodal network
- diffusion model
