Special Session on ICME 2023

Multimedia-based Health Computing

Scope, aims and topics

In healthcare, an immense amount of data is being generated by distributed sensors and cameras, as well as multi-modal digital health platforms that sup- port multimedia, such as audio, video, image, 3D geometry, and text. The availability of data from medical devices and digital record systems has greatly increased the potential for automated diagnosis. The past several years have witnessed an explosion of interest, and a dizzyingly fast development, in com- puter aided medical investigations using MRI, CT, X-rays, images, point clouds etc. Researchers, having reached a deeper understanding of the methods and on one hand are proposing elegant ways to better integrate machine learning with neural networks in complex problems (such as image reconstruction), and on the other hand are advancing the learning algorithms themselves. Note that medical imaging data may include 2D images, image volumes, and 3D geometric data (e.g., point cloud) etc. 

Nowadays, machine learning and deep learning has spread over almost all fields. Yet, pattern recognition, computer vision and language processing etc remain the heart of these advances when it comes to the multimedia data in in digital health, offering the big data and enabling advanced multimedia computing solutions to be developed. 

This special session focuses on various computing techniques (including mo- bile solutions and hardware solutions) for multimedia data in health. The topics of interest include (but are not limited to) the following areas: 

Important Dates

Regular Paper Submissions (including Special Sessions)15-Dec-22 

Regular Paper Acceptance Notification12-Mar-23

This will be the same as ICME 2023 “Important Dates” for conference papers.

Please submit using the guidelines in ICME 2023.


Xuequan Lu, Deakin University

Imran Razzak, University of New South Wales

Mingqiang Wei, Nanjing University of Aeronautics and Astronautics

Zongyuan Ge, Monash University

Liang Wan, Tianjin University