Abstract
Medical imaging plays a pivotal role in patient management in modern healthcare, with most patients who are treated in hospitals undergoing imaging procedures. These technologies can visualise anatomy and function in virtually every organ system in the body in intricate detail. There are numerous medical imaging modalities available; they vary in complexity and sophistication, from plain digital chest X-rays to simultaneous functional and anatomical imaging with positron emission tomography (PET) and computed tomography (CT) imaging (PET-CT). The challenge now is how to maximize the extraction of meaningful information from the images and present meaningful information to the users. There needs to be strategies to harness knowledge from vast image datasets and complementary sources like image sequences, text reports, and genomics. Fortunately, the era of artificial intelligence (AI) is fuelling the growth of smart decision support and analysis tools for medical image analysis. Despite rapid advancements in integrating AI algorithms into clinical decision support systems, we are still in the nascent stages of the AI revolution in medical imaging. This talk will present our research on multi-modal AI to integrate imaging and complementary data for disease modelling, analysis and visualization, aimed at improving the understanding, in an intuitive way.
About the speaker
"Jinman Kim is a Professor of Computer Science at the University of Sydney. He received his PhD from the University of Sydney in 2006 and was an Australian Research Council (ARC) Postdoctoral Research Fellow at the University of Sydney and then a Marie Curie Senior Research Fellow at the University of Geneva prior to joining the University of Sydney in 2013 as a faculty member. In 2024, he was a visiting professor at the Centre for Informatics at the University of Geneva, Switzerland. He is currently an ARC industry fellow, closely collaborating with his industry partner, Royal Prince Alfred Hospital, to conduct translational research. He is also the research director for Nepean AI research group at the Nepean Hospital.
Prof Kim leads the Biomedical Data Analysis and Visualisation (BDAV) Lab at the School of Computer Science, pioneering research on the intersection of multimodal AI with biomedical data. His research focuses on includes biomedical visual-language representations, image-omics, multi-modal data processing, and biomedical mixed reality technologies. He has produced a number of publications in this field and received multiple competitive grants and scientific recognitions."
