Multimedia Computing for Health
15-19 July 2024 | Niagara Falls, ON, Canada
Scope
In healthcare, an immense amount of data is being generated by distributed sensors and cameras, as well as multi-modal digital health platforms that support multimedia, such as audio, video, image, 3D geometry, and textThe 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 computer aided medical investigations using MRI, CTXrays, images, point clouds etc.
This special session focuses on various computing techniques (including mobile solutions and hardware solutions) for multimedia data in health.
Topics of Interest (not limited to)
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
Paper Submissions：30-Dec-23 (extended and final)
Acceptance Notification：12-Mar-24
Portal for Submission:
https://cmt3.research.microsoft.com/ICME2024
Organizers
Xuequan Lu, La Trobe University
Imran Razzak, University of New South Wales
Feng Xia, RMIT University
Bin Sheng, Shanghai Jiao Tong University
Nilmini Wickramasinghe, La Trobe University