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Asymmetry is a crucial characteristic of bilateral mammograms (Bi-MG) whenabnormalities are developing. It is widely utilized by radiologists fordiagnosis. The question of 'what the symmetrical Bi-MG would look like when theasymmetrical abnormalities have been removed ?' has not yet received strongattention in the development of algorithms on mammograms. Addressing thisquestion could provide valuable insights into mammographic anatomy and aid indiagnostic interpretation. Hence, we propose a novel framework, DisAsymNet,which utilizes asymmetrical abnormality transformer guided self-adversariallearning for disentangling abnormalities and symmetric Bi-MG. At the same time,our proposed method is partially guided by randomly synthesized abnormalities.We conduct experiments on three public and one in-house dataset, anddemonstrate that our method outperforms existing methods in abnormalityclassification, segmentation, and localization tasks. Additionally,reconstructed normal mammograms can provide insights toward betterinterpretable visual cues for clinical diagnosis. The code will be accessibleto the public.

DisAsymNet: Disentanglement of Asymmetrical Abnormality on Bilateral Mammograms using Self-adversarial Learning