Intelligent biomedical data analysis (IBDA) is an essential issue in the application of artificial intelligence in biomedical informatics. IBDA has the capability to reveal implicit, previously unknown, and diagnostically valuable information or knowledge from large amounts of biomedical data. With the fast development of artificial intelligence and parallel computing, IBDA plays an increasingly important role in clinical decision support. IBDA involves multidisciplinary technologies such as artificial intelligence, machine learning, image processing, pattern recognition, graphics, computer vision, data mining, statistics, informatics, and visualization. It automatically extracts valuable information from a large amount of medical data and combines comprehensive medical knowledge to improve clinicians’ performance by enabling more accurate and faster diagnosis and prognosis.
IEEE IBDA’2023 aims to bring together scientists, engineers, and students from around the world to discuss the latest advances in interdisciplinary research on intelligent biomedical data analysis using machine learning and artificial intelligence techniques/methods.
Topics
Detailed research topics include, but are not limited to
- Artificial intelligence in medicine
- Machine learning models for analyzing medical data
- Data mining in healthcare
- Medical image analysis
- Medical informatics
- Intelligent medical diagnosis
- Intelligent healthcare informatics
- Biomedical knowledge
- Biomedical knowledge reasoning, extraction, and graph construction
- Explainable diagnostics support system
- Multi-modal biomedical data analysis models
- Multi-modal transformer models for biomedical data
- Biomedical data anonymization
- Privacy-aware biomedical data analysis and federated learning
- Self-supervised learning for multi-modal biomedical data
- Biomedical image/signal processing
- Sensing, detection, and recognition in biomedical image/signal
- Natural language processing and knowledge discovery in biomedical documents
- Emerging digital healthcare applications
- Mobile and cloud computing for digital healthcare
- Security, trust, and privacy in digital healthcare
Symposium Chairs
- Alan Wang
alan.wang@auckland.ac.nz
University of Auckland, New Zealand - Nikola Kasabov
nkasabov@aut.ac.nz
Auckland University of Technology, New Zealand - Yuefeng Li
y2.li@qut.edu.au
Queensland University of Technology, Australia
Programme Committee
- Andreas Kempa-Liehr, University of Auckland, New Zealand
- Sibusiso Mdletshe, University of Auckland, New Zealand
- Brady Williamson, University of Cincinnati, USA
- Guizhi Xu, Hebei University of Technology, China
- Anna Bonkhoff, Harvard Medical School, USA
- Alexander Sumich, Nottingham Trent University, UK
- Haider Raza, University of Essex, UK
- Maryam Doborjeh, Auckland University of Technology, New Zealand
- Shang-Ming Zhou, University of Plymouth, UK
- Habib Noorbhai, University of Johannesburg, South Africa
- Ahmed Alkenani, Queensland University of Technology, Australia
- Alexandr Merkin, Auckland University of Technology, New Zealand
- Ping-Ju Lin, Harvard Medical School, USA