March 23rd, 2021 
Daniela Lopez de Luise Biomedical image analysis plays a vital role in diagnosing numerous pathologies, ranging from infectious diseases to cancer. Advanced methodologies for signal and/or image processing and analysis and biomedical analytics may be a powerful tool for classifying medical data, identifying individualized health trends, and finding evolutionary trajectories between normal and non-normal cases in many medical applications.
The rapid growth in algorithms and computing power in recent years has spurred the emergence of machine learning and image processing techniques as a new tool, which is rapidly entering every aspect of our lives, from intelligent personal assistance, such as Siri, Alexa, and Google Home, to self-driving cars. The medical community has begun taking advantage of these new possibilities to create new predictive models and improve existing models. For example, novel methods applying machine learning and image processing/analysis methods that are robust and theoretically sound to efficiently and intuitively solve learning tasks have become widespread across different facets of biomedical imaging for identifying complex patterns. Likewise, advances in biomedical imaging may lead to new technologies for developing predictive models for all diseases and guide the decision on who should receive preventive therapy.
To incorporate different aspects of health monitoring, authors are invited to submit papers reporting novel imaging methods with biomedical applications—in particular, exploration and research into the development of new algorithms for biomedical image processing and analysis – to this Special Issue.
- Computer-aided diagnosis;
- Imaging biomarkers;
- Image reconstruction;
- Image registration;
- Image segmentation;
- Integration of imaging with non-imaging biomarkers;
- Interpretability and explainability of machine learning;
- Machine learning for biomedical applications;
- Advances in machine learning methods;
- COVID-19 and imaging;
- Biomedical and biological image processing;
- Deep learning for biomedical imaging;
- Histopathological image analysis;
- Mixed, augmented, and virtual reality;
- Visualization in biomedical imaging.
Survey papers and reviews are also welcomed.
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 papers will be 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 2000 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.
Dr. Ioannis A. Kakadiaris, University of Houston, Houston, TX, USA, ikakadia@central.uh.edu
Assistant Professor
Department of Communication and Digital Media
University of Western Macedonia
GR 52100, Kastoria, Greece
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