Long term XENON customer, Sydney Neuroimaging Analysis Centre (SNAC) with XENON and NVIDIA presented at a recent conference on the "AI Hospital" on 25-November 2021.
The talk examined how privacy can be preserved with AI medical imaging, using a Federated Learning model.
This talk examines key issues of implementing machine learning models in a health care setting, including:
• Traditional machine learning models have limitations in privacy and security of the source data sets.
• Data Heterogeneity – Medical data is inherently diverse (type, dimensionality etc). This poses a challenge if data is not independent and identically distributed across participants.
• Traceability and accountability requires a high level of execution integrity and traceability – while ensuring privacy and security.
• Localisations of systems architecture at each institution requires AI infrastructure to be available at each site – with standardised data, labelling and training protocol.
This video covers these issues and covers SNAC’s journey from Conceptualisation to Commercialisation. Presented by:
• Dr Ettikan Karuppiah, Director/Technologist at NVIDIA
• Dr Chenyu (Tim) Wang, Director of Operations at Sydney Neuroimaging Analysis Centre (SNAC)
• Dr Werner Scholz, CTO and Head of R&D, XENON
This 30 minute video is both instructive and informative for medical institutes looking to implement AI, and deliver real benefits to patients.
