Developing Interpretable Machine Learning Models For Clinical Imaging And Single-cell Genomics
Funder
National Health and Medical Research Council
Funding Amount
$1,312,250.00
Summary
Machine learning methods will be vital to make best use of the deluge of data generated by high-throughput technologies in biomedical science. To get the most out of these models, however, we need to be able to unpack the 'black box'. I will use curated clinical and public research data to benchmark and develop interpretable deep learning models and software tools. These models will be used for breast cancer screening programs and for analysis of complex, large-scale single-cell genomics data.
Understanding and Managing the Occupational Health Impacts on Investigators of Internet Child Exploitation. Through developing best practice models for managing vicarious trauma in investigators of Internet child exploitation, the project will result in enhanced job satisfaction and less burnout of workers, and reduced health costs for policing organisations. Thus the project addresses a serious occupational health issue and contributes to the goal of promoting and maintaining good health. Furth ....Understanding and Managing the Occupational Health Impacts on Investigators of Internet Child Exploitation. Through developing best practice models for managing vicarious trauma in investigators of Internet child exploitation, the project will result in enhanced job satisfaction and less burnout of workers, and reduced health costs for policing organisations. Thus the project addresses a serious occupational health issue and contributes to the goal of promoting and maintaining good health. Further, by better managing the occupational health of investigators, the project will enhance the capacity of police organisations to deliver on their mission of investigating and preventing Internet child exploitation. This in turn contributes to the reduced consumption of Internet child exploitation and the associated traumatisation of abused victims. Read moreRead less