Join A/Prof Davis McCarthy as he showcases the BRAIx project—an innovative academic-clinical partnership leveraging deep learning to enhance early detection and personalise screening pathways.
Using data from more than 95,000 women and advanced ensemble neural networks, BRAIx generates calibrated risk scores from mammogram images, offering a powerful tool for identifying high-risk individuals and tailoring screening intervals.
Gain insights into the limitations of current screening programs and how AI can address challenges such as false positives, missed cancers, and flat participation rates.
This webinar highlights the potential of BRAIx to transform population-level screening into a more precise, risk-based model, with real-world validation across Australian and Swedish cohorts.
Don’t miss this opportunity to explore the future of AI-driven healthcare and its life-saving implications for breast cancer prevention.
Allison's research aims to use large linked datasets including general practice medical record data to improve the timely diagnosis of cancer. Her background is in biostatistics, and her research interests include novel statistical methodologies, the translation and implementation of research, and meta-research. In addition, she has been a Health Services Researcher at the Peter MacCallum Cancer Centre since 2012, where she is involved in the development and execution of statistical analysis plans, and advising on study design and methodology.
A/Prof McCarthy has contributed to popular methods for the analysis of differential expression in RNA-sequencing data, and recently developed methods for the analysis of single-cell RNA-seq data.
A/Prof McCarthy primarily implements methods in the R and Python languages and publishes open-source software packages through the Bioconductor project.He is interested in studying the effects of DNA variation on gene expression measured in individual cells. He explores single-cell genetics in two ways: by studying effects of common DNA variation on single-cell gene expression (single-cell quantitative trait locus mapping) and by studying the effects of somatic DNA mutations on single-cell gene expression (clonal cell populations). The former provides information about genetic regulation of natural gene expression variation, while the latter informs us about the effects of DNA accumulated mutations in tissues that are relevant both to healthy ageing and to cancer.
A/Prof McCarthy enjoys collaborating with biologists and other researchers to contribute computational and data analysis expertise to biologically focused studies.
