Progress and Challenges in Biomedical Image Analysis—2nd Edition
A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Medical Imaging".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 586
Special Issue Editors
Interests: AI for healthcare; cardiac digital twins; medical imaging; multimodal AI
Special Issues, Collections and Topics in MDPI journals
Interests: medical data analysis; robust and interpretable AI; multi-modal data analysis and AI
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The Editors are grateful to the many researchers who contributed to the success of the first volume of this Special Issue (https://www.mdpi.com/journal/jimaging/special_issues/2112PXP61G). We are now very pleased to announce the second edition: “Progress and Challenges in Biomedical Image Analysis—2nd Edition”.
We are currently seeing a growing interest in the dynamic and rapidly evolving field of biomedical image analysis, which plays a vital role in a wide range of healthcare applications, ranging from diagnostics to identifying individualized health trends or treatment. With the development and progress that have been made in biomedical imaging technology, biomedical imaging has become an essential tool in daily medical diagnostics. In addition, transformational analytics tools, especially artificial intelligence (AI) techniques and capabilities, are being made more accessible to researchers and healthcare. This has led to medical image analysis becoming more and more important for both research and clinical medicine/healthcare communities. From traditional radiological imaging to cutting-edge techniques, this Special Issue seeks to create a platform for researchers to not only showcase their latest advancements but also share invaluable insights and collectively address challenges in biomedical image analysis through review papers. Beyond individual contributions, this Special Issue aspires to catalyze a transformative impact on digitization (including AI) in healthcare, with a particular focus on personalized medicine.
By inviting authors to share their expertise and research findings, the initiative aims to shape the future landscape of biomedical image applications. The overarching goal is to create a knowledge-sharing hub that accelerates progress, fostering innovation in biomedical image analysis and ensuring its continued relevance in the broader context of the evolution of healthcare.
Dr. Lei Li
Dr. Zehor Belkhatir
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. Journal of Imaging 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 1800 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 image
- image segmentation
- image registration
- image classification
- advances in machine/deep learning (e.g., federated learning)
- foundation models in medical imaging
- digital twins
- image-based personalized medicine
- integration of imaging and non-imaging data (multi-modal analysis)
- explainable and interpretable AI
- radiomics analysis
- computer-aided diagnosis and surgery
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