The overarching objective of this project is to optimize and personalize breast cancer screening by advancing artificial intelligence (AI) algorithms. Breast cancer is a major global health concern. Early diagnosis is key to reducing disease burden and mortality, making breast cancer one of the few cancers regularly screened for asymptomatic women. Accordingly, the American Cancer Society recommends annual screening for women age 40 and older, leading to a substantial number of scans conducted yearly. Mammography, with an estimated 39 million annual screenings in the US, is the primary breast cancer screening method. Digital Breast Tomosynthesis (DBT) or 3D mammography has become a routine screening method for its 3D tissue view, improving cancer detection. However, the complexity of mammograms, the high radiologist workload, and the low prevalence of breast cancer in screening examinations challenges radiologists’ performance and increases the risk of false positives and missed diagnoses. Computer-aided detection (CAD) systems have been introduced to assist radiologists, but suffer from high false positives, straining healthcare systems and causing needless worry for patients. Recent advancements in deep learning (DL), a subfield of AI, have enabled the development of new CAD (AI-CAD) systems that can transform clinical decision-making. However, AI-CAD faces limitations when dealing with DBT images. Current DL models often rely on just current scans, lack real-world generalizability, and struggle with the high-resolution DICOM format and complexity of DBT images. To enhance breast cancer screening, we propose a more realistic AI-CAD system, which mimics a radiologist's approach to breast cancer screening by comparing current and prior mammograms, incorporating clinical risk factors, and accounting for image configuration variations. Furthermore, we consider practical implementation of such a system for clinical use. This project aims to achieve its main objective through three specific goals: i) to achieve a robust, explainable, and efficient representation of DBT images for cancer identification by employing self attention graph learning models; ii) to develop a multimodal longitudinal learning model for integrating history images and clinical information, thereby creating a more human-like intelligence CAD system to improve breast cancer detection and localization; iii) to practically implement AI-CAD for clinical use by utilizing expert-in- the-loop continual learning to ensure model adaptivity to changes in practice, and an algorithm-hardware codesign model compression to ensure computational feasibility. Furthermore, the project will provide a comprehensive DBT database. In this project both publicly available and in-house DBT images will be used to train, evaluate, and validate the proposed models. Due to the widespread use of mammography and critical importance of early detection in patient survival, successful completion of this project ...