Trustworthy AI for breast cancer screenings

We are developing machine learning models to further improve and streamline the national breast screening programme.

Breast cancer is the most common type of cancer among women in Norway. Early detection is key in order to limit the severe cases of the disease and ensure higher survival rates. Because of this, all women in Norway aged 50 to 69 are invited to participate in a biennial x-ray examination through the national screening programme, BreastScreen Norway.

Here, images are taken of both breasts and subsequently examined by two independent radiologists who specialise in mammography. As less than 1% of mammograms show signs of cancer, doctors spend a lot of time examining images of healthy people.

Technological advancements have made it possible to automise parts of the screening process, and current research shows that machine learning models are just as accurate as human experts. Automisation frees up time and resources, making it possible for doctors to focus on patients in need of further examination and those who are actually ill.

A two-stage method for accurate detection

It is crucial that the models aren’t so-called black boxes generating conclusions without transparency. The models should also allow for a degree of uncertainty and be able to localise possible signs of breast cancer. This is beneficial in two notable ways. Firstly, the models become more transparent and understandable. Secondly, the models themselves benefit from the explanations.

We have developed two cooperating models, where the first identifies the area of the image that most resembles cancer, and the other model zooms in and examines the area more closely. Our two-stage method has been presented at the NORA conference and our results are published in the scientific journal Nordic Machine Intelligence. Our model showed results that were on par with a radiologist, and outperformed the commercial product we compared it with.