MammoDG: Generalisable Deep Learning Breaks the Limits of Cross-Domain
  Multi-Center Breast Cancer Screening
Yijun Yang Shujun Wang Lihao Liu Sarah Hickman Fiona J Gilbert Carola-Bibiane Schönlieb Angelica I. Aviles-Rivero
Breast cancer is a major cause of cancer death among women, emphasising the
importance of early detection for improved treatment outcomes and quality of
life. Mammography, the primary diagnostic imaging test, poses challenges due to
the high variability and patterns in mammograms. Double reading of mammograms
is recommended in many screening programs to improve diagnostic accuracy but
increases radiologists' workload. Researchers explore Machine Learning models
to support expert decision-making. Stand-alone models have shown comparable or
superior performance to radiologists, but some studies note decreased
sensitivity with multiple datasets, indicating the need for high generalisation
and robustness models. This work devises MammoDG, a novel deep-learning
framework for generalisable and reliable analysis of cross-domain multi-center
mammography data. MammoDG leverages multi-view mammograms and a novel
contrastive mechanism to enhance generalisation capabilities. Extensive
validation demonstrates MammoDG's superiority, highlighting the critical
importance of domain generalisation for trustworthy mammography analysis in
imaging protocol variations.