The 1st International Workshop on Multimedia Computing for Health and Medicine
ACM MM 2024, Melbourne, Australia
Scope
Topics of Interest (not limited to)
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
Important Dates
Workshop paper submission: July 19, 2024
Workshop paper notification: August 5, 2024
Workshop paper camera-ready: August 19, 2024
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.
Paper length: The same format & template as the main conference, but the manuscript’s length is limited to one of the two options: a) 4 pages plus 1-page reference; or b) 8 pages plus up to 2-page reference.
Papers have to be submitted via https://openreview.net/group?id=acmmm.org/ACMMM/2024/Workshop/MCHM For more information, please visit our website: https://visualcom-group.github.io/mchm-24/
Keynote Speakers
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.
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.
Jie Yang, Harvard University, USA
Chiranjibi Sitaula, University of Melbourne, Australia
Lydia Cui, La Trobe University, Australia
Yanming Zhu, Griffith University, Australia
Xin Tan, East China Normal University, China
Shasha Mao, Xidian University, China
Lei Lv, Shandong Normal University, China
Shuiqiao Yang, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
Kan Chen, Singapore Institute of Technology, Singapore
Will Pan, OPT Machine Vision, China
Fang-Lue Zhang, Victoria University of Wellington, New Zealand
Kyle Wang, Volpara Health, New Zealand
Organizers
Xuequan Lu, La Trobe University, Australia
Wenxi Yue, University of Sydney, Australia
Imran Razzak, University of New South Wales, Australia
Kun Hu, University of Sydney, Australia
Jinglei Lv, University of Sydney, Australia
Sen Zhang, University of Sydney, Australia
Junhui Hou, City University of Hong Kong, China
Zhiyong Wang, University of Sydney, Australia
Jiebo Luo, University of Rochester, USA
Wei Xiang, La Trobe University, Australia
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