The 1st International Workshop on Multimedia Computing for Health and Medicine

overview

In health and medicine, an immense amount of data is being generated by distributed sensors and cameras, as well as multimodal digital health platforms that support multimedia, such as audio, video, image, 3D geometry, and text. The availability of such multimedia 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, CT, X-rays, images, point clouds etc. This proposed workshop focuses on various multimedia computing techniques (including mobile solutions and hardware solutions) for health and medicine, which targets real-world data/problems in healthcare, involves a large number of stakeholders, and is closely connected with people's health.

Scope and Topics

This workshop focuses on various computing techniques (including mobile solutions and hardware solutions) for health and medicine. The topics of interest include (but are not limited to) the following:

Digital twins for health and medicine
Digital persons for health and medicine
Developing better deep learning based debugging methods and tools
Intelligent medical and health systems
Novel theories and methods of deep learning for medical imaging
Drug discovery with deep learning
Pandemic (e.g., COVID-19) management with deep learning
Health and medical behavior analytics with deep learning
Medical visual question and answering
Un/self/semi/weakly/fully-supervised medical data (text/images)
Graph learning on medical data (text/images)
Generating diagnostic reports from medical images
Fewer Labels in clinical informatics
Summarization of clinical information
Knowledge transfer under various clinical environments
Multimodal medical image analysis
Medical image registration
Organ and lesion segmentation/detection
Image classification with MRI/CT/PET
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 in Medical computer vision
User/patient psychometric modeling from video, image, audio, and text
Medical foundation models (FMs)
Transfer learning and model fine-tuning for medical data
Activity detection or recognition on medical sequential data (e.g., surgical videos, EEG)
Sequential pattern recognition of medical data (e.g., surgical videos, EEG, MRI/CT)
Image/video/language/audio decoding from medical data (e.g., MRI and EEG)
Medical dataset and benchmark contribution
Medical data synthesis
Medical data augmentation
Model robustness in medical data analysis
Model trustworthiness and explainability in medical data analysis
Ethical considerations on AI for medical data analysis

programme

TBD

KEY DATES FOR WORKSHOP

Workshop paper submission deadline: July 29, 2024 (New)

Workshop paper notification: August 12, 2024 (New)

Workshop paper camera-ready: August 19, 2024

Submission

We invite submissions of original research papers addressing but not limited to the topics as listed above. Submissions should adhere to the ACM Multimedia 2024 formatting guidelines and will undergo a rigorous peer-review process. Accepted papers will be presented at the workshop and included in the ACM Digital Library. We also welcome submissions of demos, datasets, and position papers that contribute to the workshop's themes. The template can be found via:

Page limits:

Workshop full papers: 8 page limit + 2 extra pages for references only, no presentation video
Workshop short papers: 4 page limit + 1 extra page for references only, no presentation video

Papers have to be submitted via:

Keynote Speakers

Wei Chen

Prof. Wei Chen is Head of School of Biomedical Engineering and Professor at the University of Sydney, Australia. She is the Associate Editor of various IEEE journals and the newly elected IEEE EMBS AdCom Asia/Pacific representative. From 2020 to 2022, she was the Chair of IEEE Sensor and Systems Council China Chapter and Managing Editor of IEEE Reviews in Biomedical Engineering. She has published 2 books, 200+ scientific papers, holds 20+ granted patents, and led 10+ important R&D projects. Her research focuses on biomedical sensor systems and health informatics.

Luping Zhou

Dr Luping Zhou is an Associate Professor in School of Electrical and Information Engineering, the University of Sydney. She obtained her PhD from Australian National University and got her post-doctoral training in University of North Carolina at Chapel Hill. Dr. Zhou works on the interface of medical image analysis, machine learning, and computer vision, and has published 100+ research papers in these fields. Her current research is focused on medical image analysis with statistical graphical models and deep learning, as well as general visual recognition problems. She was a recipient of the prestigious ARC DECRA award. Dr. Zhou is the Associate Editor of the journals IEEE Trans. on Medical Imaging and Pattern Recognition.

Hamid Laga

Dr Hamid Laga is a Professor in Murdoch University, Australia. His main expertise is in Machine Learning, 3D Computer Vision, and Computer Graphics. While his primary focus is on fundamental research, he undertakes cross-disciplinary and translational research across health and agriculture. He also provides consultancy services to industries and companies interested in translating research outcomes to end-user products.