Developing machine learning models for MRI-based precision medicine for breast cancer

Around 25% of all women who are diagnosed with breast cancer eventually die from the disease. The treatment of breast cancer patients can be improved.

We have conducted retrospective studies demonstrating that AI models may reach radiologist-level performance for screening mammography. The proposed project is aiming at the diagnostic process following detection.

I plan to develop AI models based on magnetic resonance imaging (MRI), which provide images richer in information compared to mammogrpahy.The project will leverage my postdoc experience with machine-learning models for breast MRI and my continued collaboration with KTH in terms of state-of-the-art AI models.

In our joint AI model development for mammography, with KTH, we used convolutional neural networks.

Recently, a new approach, vision transformer has been shown to be able to outperform the convolutional neural networks for image-based tasks.

Therefore, my plan is to apply vision transformers to breast MRI images to address three important areas in the diagnostic process for breast cancer: image segmentation aiding radiologists to identify anatomic structures in the MRI images; radiology-pathology correlation to ascertain that biopsies correlate with image findings; therapy response prediction to inform the choice of neoadjuvant therapy.

The research will tie into a recently approved EU horizon project where we will gain access to a common pool of breast cancer imaging data, and for which I am the Swedish PI.