$11.4 million to advance wellbeing and solve big problems
Friday, 10 November 2017
University of Adelaide researchers will use machine learning techniques to find the earliest signs of disease that are difficult to detect visually from a CT scan.
This Discovery Project was announced today as one of 32 projects with total funding of $11.4 million, awarded to the University of Adelaide by the Australian Government through the Australian Research Council’s National Competitive Grants Programme.
This project, awarded $387,884 over three years, will advance medical image analysis to discover visual bio-markers from chest CT scans, to predict five-year survival of chronic disease.
“We will be using the chest CT scans of patients who presented with disease symptoms which weren’t visible in a CT scan, but who subsequently died within five years,” says chief investigator Associate Professor Gustavo Carneiro, from the University of Adelaide’s Australian Centre for Visual Technologies in the School of Computer Science.
“Using machine learning, we will be able to classify which CT scans are from patients who would die in five years and, while not diagnosing the disease, we’ll be able to highlight the particular areas of concern to enable further investigation. This would enable doctors to intervene at a much earlier stage of the disease than is being currently detected.”
Another Discovery Project grant of $423,102 over three years was awarded to Professor Anton van den Hengel, Director of the Australian Centre for Visual Technologies to shift artificial intelligence to more human-like thinking. He heads a world-leading group of more than 60 researchers working in computer vision and artificial intelligence.
“Artificial intelligence has made great leaps over the past 10 years with much of the progress due to a form of artificial intelligence called deep learning,” says Professor Van den Hengel.
“Deep learning is how Facebook finds your face in photos and how a driverless car is steered. But one of the limitations is that it is purely reactive. This project will enable reason and a higher level analysis than previously possible.”
The University of Adelaide was awarded 21 new Discovery Projects ($8.3 million); seven new Discovery Early Career Researcher Award (DECRA) grants ($2.55 million), one Discovery Indigenous project ($512,688); and three projects totaling $942,881 funding under the Linkage Infrastructure Equipment and Facilities (LIEF) scheme.
“These funding outcomes add to what has already been an outstanding year of ARC funding success for the University of Adelaide,” says Acting Deputy Vice-Chancellor Research Professor Julie Owens.
“What’s particularly pleasing is the number of projects – at least nine - using machine learning, artificial intelligence or advanced mathematical modelling to create novel technologies and advance knowledge to solve challenging issues in industry, health, the environment and other areas.
“This success recognises the world-leading capability the University of Adelaide has in these and other areas of great importance to our society.”
Today’s grant success follows the announcements earlier this year of $12.91 million of ARC funding for two prestigious Australian Laureate Fellowships, one Industrial Transformation Training Centre, four Future Fellowships and two Linkage Projects.
Other major projects announced today include:
• $512,688 to Dr Raymond Tobler (Australian Centre for Ancient DNA and School of Biological Sciences) for an Discovery Indigenous project: Using genomic DNA to reveal the peopling of the Sahul
• $216,272 to Professor Carol Johnson (School of Social Sciences) for a Discovery Project: In the frame: Analysing Liberal and Labor government gender equality policy.
• $413,298 to Associate Professor Veronica Soebarto (School of Architecture and Built Environment) for a Discovery Project: Improving thermal environment of housing of older Australians.
• $520,496 to Associate Professor Matthew Tucker (School of Agriculture, Food and Wine) for a Discovery Project: Safeguarding seeds by enhancing female germline development in plants.
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