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:
Developing better deep learning based debugging methods and tools
Intelligent medical and health systems
Novel theories and methods of deep learning for medical imaging
Pandemic (e.g., COVID-19) management with deep learning
Health and medical behavior analytics with deep learning
Dealing with the vulnerability to adversarial examples
Medical visual question and answering
Un-/semi-/weakly/fully supervised medical data (text, images etc)
Graph learning on medical data (text, images etc)
Generating diagnostic reports from medical images
Fewer Labels in clinical informatics
Summarization of clinical information
Knowledge transfer and domain adaptation under various health environments
Multimodal medical image analysis
Medical image registration
Organ and lesion segmentation/detection
Image classification with MRI/CT/PET etc
Medical image enhancement/denoising
Learning robust medical image representation with noisy annotation
Predicting clinical outcomes from multimodal medical data
Anomaly detection in medical images
Active Learning and Life-long Learning
User/patient psychometric modeling from video, image, audio, and text
Important Dates
Regular Paper Submissions (including Special Sessions)：15-Dec-22
Regular Paper Acceptance Notification：12-Mar-23
This will be the same as ICME 2023 “Important Dates” for conference papers.
Please submit using the guidelines in ICME 2023.
Organizers
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