Computational pathology (CPATH)/Computer Assisted Diagnosis research in breast cancer.
Problem:
Diagnosing breast cancer accurately and fairly is a challenge, and using a microscope alone doesn’t always catch unique patterns in cancer cells that could help with treatment.
Project:
Dr. Gavin Harris is using AI and machine learning to analyze breast cancer tissue samples. This will allow computers to identify cancer patterns automatically, especially for a specific type of breast cancer (HR+)/(HER2-), while also ensuring that these tools are fair and ethical.
Outcome:
The project will make it easier to assess likely breast cancer aggressiveness more easily, accurately and at a lower cost helping doctors choose the best treatment options for each patient based on their unique cancer type.
Future:
This work could lead to more personalised breast cancer treatments, allowing patients to get therapies that are better suited to their specific needs, which may improve their health outcomes.
Keep reading
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From Evidence to Action: Expanding the ‘Not a One-Size-Fits-All’ Breast Cancer Screening Model for Aotearoa New Zealand.
Problem:Breast cancer screening in Aotearoa New Zealand does not benefit all women equally. Many cancers are still found outside the national screening programme, and Māori and Pacific women can fa...
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 pa...
“Shielding” Macrophages: Uncovering Immune-Mediated Chemoresistance in Triple-Negative Breast Cancer
ProblemTriple-negative breast cancer (TNBC) is one of the hardest types to treat and affects Māori and Pacific women more than others. Many patients with TNBC don’t fully respond to chemotherapy, a...
