Using Deep Learning And Digital Pathology To Intrinsically Subtype Breast Cancer
Problem
Traditional pathology uses microscopes to examine tissue slides, but this method can be slow and inconsistent, and not all patients can afford advanced molecular testing.
Project
Digital pathology, which involves reviewing tissue slides on a computer, is becoming more popular. Researchers want to develop machine-learning algorithms that can automatically detect important genomic changes in breast cancer from these digital slides.
Outcome
These algorithms could provide crucial molecular data without the need for expensive tests, making it easier for all patients to get accurate diagnoses.
Future
The ultimate goal is to use this technology to tailor treatments to each patient, improving their chances of the best possible outcome.
Project Update
This project successfully showed that computational pathology (CPATH), which moves diagnosis from a microscope to a computer, can be developed in New Zealand. By using AI technology on breast cancer samples, it helps pathologists quickly create accurate reports and provides important molecular data without expensive tests.
This approach aims to make diagnosis more detailed and accessible for everyone, improving health equity.
Dr. Gavin Harris will continue this work with further funding.
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