DisAsymNet：基于自对抗学习的双侧乳腺X线影像非对称异常解耦
DisAsymNet: Disentanglement of Asymmetrical Abnormality on Bilateral Mammograms using Self-adversarial Learning
AI总结：
本文提出DisAsymNet框架，通过非对称异常变换器引导的自对抗学习解耦双侧乳腺X线影像中的异常与对称结构，在分类、分割和定位任务上优于现有方法，并提升诊断可解释性。
摘要：
当异常发展时，非对称性是双侧乳腺X线影像（Bi-MG）的一个关键特征，放射科医生广泛利用这一特征进行诊断。“当非对称异常被移除后，对称的双侧乳腺X线影像会是什么样子？”这一问题在乳腺X线影像算法的发展中尚未得到足够关注。解决这一问题可以为乳腺解剖结构提供有价值的见解，并有助于诊断解读。因此，我们提出了一个新框架DisAsymNet，该框架利用非对称异常变换器引导的自对抗学习来解耦异常与对称的双侧乳腺X线影像。同时，我们提出的方法部分由随机合成的异常进行引导。我们在三个公开数据集和一个内部数据集上进行了实验，并证明我们的方法在异常分类、分割和定位任务上优于现有方法。此外，重建的正常乳腺X线影像可以为临床诊断提供更具可解释性的视觉线索。代码将向公众开放。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
