Machine Learning Algorithms for Biomedical Image Analysis and Applications
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems".
Deadline for manuscript submissions: 31 December 2024 | Viewed by 2624
Special Issue Editors
Interests: clinical data analytics; healthcare decision support systems
2. Department of Computer Science and Technology, University of Cambridge, Cambridge CB2 1TN, UK
Interests: medical image analysis; radiomics; machine learning; explainable AI; multimodal learning
Interests: medical images analysis and quantification; radiomics; applied machine learning, explainable predictive models
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, the infusion of architectural and algorithmic innovations within the realm of machine learning has revolutionized medical image analysis. Despite these advancements, the assimilation of these models into clinical practice bears several challenges. The wide availability and heterogeneity of open data offer the opportunity to train increasingly ambitious models, but which harbor a myriad of pitfalls.
Such challenges include the need for multimodal training, data harmonization, training small dataset scenarios, etc. Additionally, stringent requirements for explainability and reliability imposed by regulatory agencies add further complexity to the integration of machine learning models in clinical settings. Addressing these challenges holds immense potential for transformative impacts on healthcare, particularly in advancing the concepts of precision and personalized medicine.
This Special Issue will provide a forum to publish original research papers covering state-of-the-art and novel algorithms, methodologies, and applications of computational methods for biomedical image analysis and quantification, as well as to implement predictive models for precision medicine and clinical decision support systems.
Topics of interest include, but are not limited to, the following:
- Biomedical image analysis algorithms and applications;
- Machine learning and deep learning methods for medical image analysis;
- Multimodal learning;
- Data integration;
- Data harmonization;
- Radiomics;
- Explainable AI techniques for interpretable and transparent AI.
Dr. Ines Prata Machado
Dr. Francesco Prinzi
Dr. Carmelo Militello
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. Algorithms is an international peer-reviewed open access monthly 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 1600 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
- biomedical imaging
- precision medicine
- health informatics
- radiomics
- machine-learning
- deep-learning
- clinical decision support systems
- explainable artificial intelligence
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