The University of Queensland's (UQ) high performance computer system, Weiner, is now able to apply image processing and deep learning algorithms for digital pathology.
According to Brian Lovell, UQ professor at the School of Information Technology and Electrical Engineering, as Weiner can run a number of graphics processing units in parallel, this has sped up artificial intelligence (AI) training for pathology tests so it is hundreds of times faster than before.
This will allow the medical industry to expedite their work as almost 70% of general practitioner diagnoses are based on pathology tests, Lovell added.
"Developing AI for digital microscopy in pathology is an iterative process requiring extensive validation and standardisation," Sullivan Nicolaides Pathology (SNP) CEO Michael Harrison said.
"These advances will significantly hasten the development of AI algorithms in digital pathology and enable earlier movement into the routine pathology setting.
"We see AI as augmenting the quality and efficiency of pathology rather than replacing pathologists and scientists, and it is successfully being used at SNP to augment the quality and interpretation of some testing."
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The enhancements made to Weiner will have applications in all areas of microscopy in pathology, including immunology, histopathology, and microbiology specialities.
Weiner is already being used to analyse human skull models at UQ's Queensland Brain Institute, with the work conducted being dedicated towards delaying the onset of one of the world's most debilitating illnesses -- Alzheimer's Disease.
The Queensland Brain Institute has been using the Wiener system to model the behaviour of ultrasound using an analysis technique called Finite Element Method, with the institute hoping that ultrasound can potentially be used to temporarily allow for the direct delivery of therapeutic drugs to the brain.
Weiner boasts visualisation nodes that are comprised of: Two Dell EMC PowerEdge R740 HPC nodes featuring two Intel Xeon Gold 6132, 28 cores per node; 1TB DDR4 RAM; three Nvidia Tesla V100 Accelerator units; 1.6TB Dell EMC NVMe flash storage; and 100Gbps Mellanox EDR InfiniBand HCA's.
The compute and analysis nodes boast 17 Dell EMC PowerEdge R740s, featuring two Intel Xeon Gold 6132, 28 cores per node; 384GB DDR4 RAM; two Nvidia Tesla V100 Accelerator Units; 1.6TB Dell EMC NVMe Flash Storage; and 100Gbps Mellanox EDR InfiniBand HCA's.
It also has nodes that are comprised of: 15 Dell EMC PowerEdge C4140, featuring two 2 x Intel Xeon Gold 6132, 28 cores per node; 384GB DDR4 RAM; four Nvidia SXM2 Tesla 32GB V100 Accelerator Units; 1.6TB Dell EMC NVMe Flash Storage; and 100Gbps Mellanox EDR InfiniBand HCA's.