Developing interpretable machine learning models for clinical imaging and single-cell genomics

Funding Activity

Website
http://purl.org/au-research/grants/nhmrc/1195595

Funding Status
Status not available

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Funded Activity 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.

Funded Activity Details

Start Date: 01-01-2020

End Date: 01-01-2025

Funding Scheme: Investigator Grants

Funding Amount: $1,312,250.00

Funder: National Health and Medical Research Council

Research Topics

ANZSRC Field of Research (FoR)

ANZSRC Socio-Economic Objective (SEO)

There are no SEO codes available for this funding activity

Other Keywords

bioinformatics | cancer detection | imaging | statistics

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