BDAMI 2024
IEEE Workshop on Big Data Analytics for Medical Imaging
In conjunction with IEEE Big Data 2024 Washington DC, USA
SPECIAL ISSUE APPROVED!!!
"AI-guided Big Data Analytics for Medical Imaging" on Multimedia Tools and Applications (MTAP) Springer.
INTRODUCTION
Medical imaging technology has resulted in an unprecedented volume of diagnostic data from a variety of sources, including computed tomography (CT), magnetic resonance imaging (MRI), radiography, and more. However, the key problem is no longer just capturing high-resolution pictures, but also effectively understanding and extracting information from this vast amount of data. This is where Big Data analytics come in.
Big Data analytics in medical imaging seeks to extract important information, detect patterns and trends, and assist critical clinical choices. This approach, further helped by machine learning and deep learning techniques not only promises to improve diagnostic and treatment accuracy, but also to increase operational efficiency in healthcare services.
This workshop aims to attract the interest of Big Data and Medical Imaging researchers in developing novel methodologies, datasets, and approaches for diagnosis, patient treatment, medical image management optimization, and misinformation and disinformation detection.
TOPICS
BDAMI is aimed at different categories of professionals interested in harnessing the potential of Big Data in medical imaging.
Medical process support
Big Data Analytics for predictive diagnostics with medical imaging
Big Data Analytics for personalised treatment with medical imaging
Big Data Analytics for medical imaging supporting hospital workflow optimisation
Data Analytics for disease progression
Medical Image security
Deepfakes detection on Medical Imaging
Misinformation and disinformation on medical imaging
Medical Image Processing
Big Data Analytics for anomalies detection on medical imaging
Big Data Analytics for medical images enhancement and reconstruction
Big Data Analytics for noise reduction in medical images
Medical Image Dataset
Curated datasets for big data analytics in medical imaging
Synthetic datasets for big data analytics in medical imaging
IMPORTANT DATES
September 20, 2024: Due date for full workshop papers submission
Nov 4, 2024: Notification of paper acceptance to authors
Nov 20, 2024: Camera-ready of accepted papers
Dec 15, 2024: Workshop (Full Online)
SUBMISSION AND REGISTRATION
Authors are invited to submit papers up to 10 pages references included (6 to 8 pages are recommended) in the IEEE 2-column format (IEEE Computer Society Proceedings Manuscript template) that can be found here.
All papers must be submitted via the conference submission system for the workshop.
Full registration of IEEE BigData 2024 is required for at least one of the authors for participating in the workshop.
Registration details and fees will be made available soon.
ORGANIZERS
Carmen Bisogni
University of Salerno, Salerno, Italy
Shaohua Wan
University of Electronic Science and Technology of China, Shenzhen, China
Marco Salvatore Zappatore
Marco Salvatore Zappatore
University of Salento, Lecce, Italy
Program Committee
To Be Updated
Matteo Polsinelli, University of Salerno, Italy
Chiara Pero, University of Salerno, Italy
Paola Barra, Parthenope University of Naples, Italy
Giorgio De Nunzio, University of Salento, Italy
Valerio De Luca, University of Salento, Italy
Antonella Calò, University of Salento, Italy
CONTACTS
For any information, please contact
Carmen Bisogni
University of Salerno
Salerno, Italy
cbisogni@unisa.it
SPECIAL ISSUE ON MULTIMEDIA TOOLS AND APPLICATIONS
"AI-guided Big Data Analytics for Medical Imaging"
The authors of chosen papers presented at BDAMI24 have the opportunity to submit an extended version of their contributions that incorporates both the reviewers' comments on their conference paper and the feedback obtained during the conference presentation. It is important to note that the extended version is expected to contain a significant scientific contribution, such as new algorithms, experiments, or qualitative/quantitative comparisons, and to not transfer large sections of the conference paper.
The link to the call for papers will be soon available.