While population-based breast cancer screening with mammography has shown to be very effective, mammography alone is not sufficient for the adequate screening of women who are carriers of genetic mutations or have other risk factors for breast cancer. In this project, we research how you use prior imaging and associated clinical information to improve the detection and classification of cancer using deep learning in these high-dimensional images. In particular, as prior information in the form of mammography, digital breast tomosynthesis or MRI is often available we will investigate how to use this information to improve the performance of the detection and classification systems. A part of the project is devoted on researching unsupervised methods to ensure stability of these models to different acquisition and machine parameters. The project is in close collaboration with clinicans and involves research to determine the optimal moment of cancer detection, the value of the developed methods for earlier cancer detection and the consequences of later detection on prognosis. In MARBLE our industrial partner Screenpoint Medical, providing valorization and the Data Science group at the Radboud University are directly involved.