Advances in Machine Learning for Biomedical Signal and Image Processing
A special issue of Journal of Imaging (ISSN 2313-433X).
Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 8321
Special Issue Editor
Special Issue Information
Dear Colleagues,
Machine learning (ML) is today being widely used as a solution to learn from data how to solve problems difficult to model in an analytical way. Due to the democratization of efficient and user-friendly computational facilities, many applications are taking advantage from ML tools. In this sense, the biomedical signal and image processing community is facing a great challenge and opportunity to integrate ML tools in applications such as diagnosis aid, reconstruction, restoration, and data analysis. From diagnosis to therapy, ML for biomedical signal and image processing is a hot research topic with high potential at both methodological and applicative levels.
The motivation behind this proposal lies in the emergence of the use of ML in the biomedical field. This Special Issue will contribute to outline recent advances in different applications handling biomedical signals and images with ML tools. The goal is to point out how ML and deep learning methods can solve various problems in biomedical signal and image processing such as segmentation, super-resolution, reconstruction, detection, etc.
The goal of this Special Issue is to bring together a number of recent methodological and applied advances in machine learning for biomedical data that are of particular interest to the signal and image processing community in view of their challenges and opportunities.
Indeed, the interest of both the machine learning and the medical imaging specialized research communities in the topic is clearly reflected by the organization of special sessions in conferences like ISBI 2020, WCCI 2020, BIOSTEC 2021, EUSIPCO 2021, and ICDHT 2021.
We request contributions presenting methodological and/or applied advances in any application handling biomedical signals or images using ML approaches. Scientifically-founded innovative and speculative research lines are welcome for proposal and evaluation.
Prof. Dr. Lotfi Chaari
Guest Editor
Manuscript Submission Information
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Keywords
- machine learning
- deep learning
- medical imaging
- biomedical signal
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