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Breast cancer is a major cause of cancer death among women, emphasising theimportance of early detection for improved treatment outcomes and quality oflife. Mammography, the primary diagnostic imaging test, poses challenges due tothe high variability and patterns in mammograms. Double reading of mammogramsis recommended in many screening programs to improve diagnostic accuracy butincreases radiologists' workload. Researchers explore Machine Learning modelsto support expert decision-making. Stand-alone models have shown comparable orsuperior performance to radiologists, but some studies note decreasedsensitivity with multiple datasets, indicating the need for high generalisationand robustness models. This work devises MammoDG, a novel deep-learningframework for generalisable and reliable analysis of cross-domain multi-centermammography data. MammoDG leverages multi-view mammograms and a novelcontrastive mechanism to enhance generalisation capabilities. Extensivevalidation demonstrates MammoDG's superiority, highlighting the criticalimportance of domain generalisation for trustworthy mammography analysis inimaging protocol variations.

MammoDG: Generalisable Deep Learning Breaks the Limits of Cross-Domain Multi-Center Breast Cancer Screening