AI Imaging at UNSW ResTech
Year: 2024
Type: article
Abstract: As AI continues to be adopted, researchers increasingly seek support for access to tools to augment and accelerate their research. At UNSW ResTech, one area we have focused on is in the imaging domain due to its interdisciplinary reach across the university. This avenue has assisted ResTech to effectively integrate AI support into our e-research infrastructure. Through collaborative projects with instrument facilities and research groups, our efforts have emphasised the use of open-source software which are enhanced through our on-prem cluster Katana ensuring accessibility and optimisation. We have integrated an assortment of AI-powered imaging frameworks that span across disciplines from medical imaging to engineering. These range from GUI-based software that incorporates MONAI and Meta AI s Segment Anything Model for medical applications, to ImageJ and ZeroCostDL4Mic deep learning frameworks for materials science and biology applications. We further support our initiatives with training of such platforms through training and in particular have partnered with NVIDIA as part of their University Ambassador program to leverage support and reputation from enterprise. The presentation will outline our strategies, tools, and collaborative efforts in integrating AI into imaging research, and explore how we are shaping the future of AI imaging at UNSW.
Author: Tung, Patrick
Institution: UNSW Sydney
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W6893367136
FWCI:
Cites:
Cited by:
Related to: