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8 November 2018

Rapid precision-scanning technology to speed up medical diagnoses and help address Australia’s shortage of trained pathologists is being developed at the University of Queensland.

The University’s team is working to replace glass pathology slides with digital slides for faster analysis, distribution and storage.

Researcher said almost 70 per cent of GP diagnoses were based on pathology tests, and the changes could revolutionise Australian laboratories.

“The pace that Australia can train pathologists is much slower than the nation’s reliance and increased usage of the limited pathology services,” he said.

“We want to increase capacity without extending work times.”

The project could also have applications in immunology, histopathology and microbiology.

A fully automated scanning system for immunology tests has already been deployed and is in use in a pathology laboratory.

The researchers are also working on new data management processes and delivery technologies to allow the digitised slides to be stored indefinitely, creating a potential data bank for further research and education.

The team — led by Õ¬Äе¼º½’s and Dr Wiliem, and Peter Hobson and Anthony Jennings — uses computer vision, machine-learning and pattern recognition methods to create scanning and image-based Computer Aided Diagnostic systems.

The group is using Õ¬Äе¼º½’s newest high-performance computer, , for its work, with the support of Õ¬Äе¼º½’s .

“The computer is fitted with the world’s most powerful graphics processing units (GPUs), allowing us to train multiple machine-learning models using large data sets,” said Dr Wiliem.

“We have found Wiener allows us to perform the training five to eight times faster than our smaller, legacy GPUs,” he said.

“This enables us to test several ideas at the same time.”

The researchers create computer image data sets from different pathology areas and after de-identifying patient data.

A startup, Viscient Pty Ltd, will continue the roll-out process and provide ongoing support of technologies developed from the project.

Media: Õ¬Äе¼º½ Researcher, Professor Brian Lovell, lovell@itee.uq.edu.au, 0411 094 380; Õ¬Äе¼º½ Researcher, Dr Arnold Wiliem, a.wiliem@uq.edu.au, +61 7 3365 1643; Õ¬Äе¼º½ Communications, Paige Ashby, p.ashby@uq.edu.au, 0430 511 615.