Multimedia Tools and Applications - Call for papers: AI-guided Big Data Analytics for Medical Imaging (1256)
Aims and Scope
Medical imaging technologies collectively produce an enormous amount of high-resolution images, which represent a significant challenge not only in terms of storage, but also of analysis and interpretation to provide accurate diagnosis and therapies. If in the past the challenge was to get high-quality images, now it has shifted to looking for methods to extract useful information from this vast pool of data. It is in this context that research on medical imaging and big data are intertwined.
Big Data analysis in medical imaging encompasses a wide range of techniques and methodologies designed to extract valuable information from large data sets. Using AI algorithms, hidden patterns can be discovered, significant trends identified, and critical clinical decision-making helped. Thanks to machine learning (ML) and deep learning (DL), it is possible to develop, from this large amount of medical images, predictive models and automated systems.
The purpose of this Special Issue is to investigate new challenges, new methods, and new applications of big data analytics for medical imaging guided by AI. The Special Issue aims at collecting contributions of contributors either from academics or industry professionals on advanced techniques that enrich the state-of-the-art on medical pattern recognition and big data applications on (but not limited to) the following topics of interest:
● Deepfakes detection on Medical Imaging
● AI-guided Data Analytics for disease progression
● Big Data Analytics for anomalies detection on medical imaging
● AI-generated datasets for big data analytics in medical imaging
● Big Data Analytics for anomalies detection on medical imaging
● AI on Big Data to personalize treatment based on medical imaging
●Big Data Analytics for medical imaging supporting hospital workflow optimization
● Benefits, challenges, and risks of using Generative AI in medical imaging
● The role of Generative AI in datasets for medical imaging
Guest Editors
Carmen Bisogni - cbisogni@unisa.it
Shaohua Wan - shaohua.wan@uestc.edu.cn
Marco Salvatore Zappatore - marcosalvatore.zappatore@unisalento.it
Submission Deadlines
Open: Immediately
Deadline: April 4, 2025
Submission Guidelines
Authors should prepare their manuscript according to the Instructions for Authors available from the Multimedia Tools and Applications website. Authors should submit through the online submission site at https://www.editorialmanager.com/mtap/default.aspx and select “SI 1256 - AI-guided Big Data Analytics for Medical Imaging” when they reach the “Article Type” step in the submission process. Submitted papers should present original, unpublished work, relevant to one of the topics of the special issue. All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process. Please note that the authors of selected papers presented at BDAMI 2024 are invited to submit an extended version of their contributions by taking into consideration both the reviewers’ comments on their conference paper, and the feedback received during presentation at the conference. It is worth clarifying that the extended version is expected to contain a substantial scientific contribution, e.g., in the form of new algorithms, experiments or qualitative/quantitative comparisons, and that neither verbatim transfer of large parts of the conference paper nor reproduction of already published figures will be tolerated. The extended versions of BDAMI 2024 papers will undergo the standard, rigorous journal review process and be accepted only if well-suited to the topic of this special issue and meeting the scientific level of the journal. Final decisions on all papers are made by the Editor in Chief.