Monash Biomedical Imaging highlights from 2021 and looking ahead to 2022
Despite the challenges COVID-19 has continued to present, Monash Biomedical Imaging (MBI) has had another outstanding year in terms of publications and scientific output. In this webinar, Professor Gary Egan, Director of MBI, will present an overview of MBI’s achievements during 2021 and outline the biomedical imaging research programs and partnerships in 2022. His presentation will cover: • MBI operational and research achievements during 2021 • Biomedical imaging technology developments and research outcomes during 2021 • Linked laboratories and research teams at MBI • Progress on the development of a cyclotron and precision radiopharmaceutical facility at Clayton • Emerging research opportunities at the Monash Heart Hospital in cardiology and cardiovascular disease. Professor Gary Egan is Director of Monash Biomedical Imaging, Director of the ARC Centre of Excellence for Integrative Brain Function and a Distinguished Professor at the Turner Institute for Brain and Mental Health, Monash University. He is also lead investigator of the Victorian Biomedical Imaging Capability, and Deputy Director of the Australian National Imaging Facility. His substantive body of published work has made a significant impact on the neuroimaging and neuroscience fields. He has sustained success in obtaining significant grants to support his own research and the development of facilities to advance biomedical imaging.
Biomedical Image and Genetic Data Analysis with machine learning; applications in neurology and oncology
In this presentation I will show the opportunities and challenges of big data analytics with AI techniques in medical imaging, also in combination with genetic and clinical data. Both conventional machine learning techniques, such as radiomics for tumor characterization, and deep learning techniques for studying brain ageing and prognosis in dementia, will be addressed. Also the concept of deep imaging, a full integration of medical imaging and machine learning, will be discussed. Finally, I will address the challenges of how to successfully integrate these technologies in daily clinical workflow.