Asymmetry is a crucial characteristic of bilateral mammograms (Bi-MG) when abnormalities are developing. It is widely utilized by radiologists for diagnosis. The question of 'what the symmetrical Bi-MG would look like when the asymmetrical abnormalities have been removed ?' has not yet received strong attention in the development of algorithms on mammograms. Addressing this question could provide valuable insights into mammographic anatomy and aid in diagnostic interpretation. Hence, we propose a novel framework, DisAsymNet, which utilizes asymmetrical abnormality transformer guided self-adversarial learning 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, and demonstrate that our method outperforms existing methods in abnormality classification, segmentation, and localization tasks. Additionally, reconstructed normal mammograms can provide insights toward better interpretable visual cues for clinical diagnosis. The code will be accessible to the public.

翻译：摘要：双侧乳腺X线摄影（Bi-MG）中，不对称性是异常发展时的关键特征，被放射科医师广泛应用于诊断。然而，“当不对称异常被去除后，对称性Bi-MG会呈现何种形态？”这一问题在乳腺X线摄影算法开发中尚未受到足够重视。解决该问题可为乳腺X线解剖学提供重要见解，并辅助诊断判读。为此，我们提出新颖框架DisAsymNet，该方法利用不对称异常Transformer引导的自对抗学习来实现异常与对称性Bi-MG的解耦。同时，我们的方法部分受随机合成异常的引导。我们在三个公开数据集和一个内部数据集上开展实验，证明该方法在异常分类、分割及定位任务中均优于现有方法。此外，重建的正常乳腺X线摄影可为临床诊断提供更具可解释性的视觉线索。相关代码将公开提供。