FedMed-ATL：通过仿射变换损失的未对准未配对脑图像合成
FedMed-ATL: Misaligned Unpaired Brain Image Synthesis via Affine Transform Loss
摘要：
完全对齐和配对的多模态神经成像数据的存在已经证明了其在脑部疾病诊断中的有效性。然而，收集完整的对齐和配对数据集是不切实际的，因为实际困难可能包括高成本、长时间采集、图像损坏和隐私问题。以前，未对齐的未配对神经成像数据（称为MUD）通常被视为噪声标签。然而，这种基于噪声标记的方法不能很好地完成时，错位的数据发生严重失真。例如，旋转角度不同。在本文中，我们提出了一种新的联邦自监督学习（FedMed）的大脑图像合成。一个仿射变换损失（ATL）制定了利用严重失真的图像，而不违反隐私法的医院。然后，我们引入了一个新的数据增强程序的自我监督训练，并将其送入三个辅助头，即辅助旋转，辅助平移和辅助缩放头。所提出的方法在严重不对齐和不成对的数据设置下的合成结果质量方面表现出了先进的性能，并且比其他基于GAN的算法具有更好的稳定性。所提出的方法还减少了对可变形配准的需求，同时鼓励利用未对准和未配对的数据。实验结果验证了我们的学习范式相比，其他国家的最先进的方法的出色表现。
英文摘要：
The existence of completely aligned and paired multi-modal neuroimaging data has proved its effectiveness in the diagnosis of brain diseases. However, collecting the full set of well-aligned and paired data is impractical, since the practical difficulties may include high cost, long time acquisition, image corruption, and privacy issues. Previously, the misaligned unpaired neuroimaging data (termed as MUD) are generally treated as noisy label. However, such a noisy label-based method fail to accomplish well when misaligned data occurs distortions severely. For example, the angle of rotation is different. In this paper, we propose a novel federated self-supervised learning (FedMed) for brain image synthesis. An affine transform loss (ATL) was formulated to make use of severely distorted images without violating privacy legislation for the hospital. We then introduce a new data augmentation procedure for self-supervised training and fed it into three auxiliary heads, namely auxiliary rotation, auxiliary translation and auxiliary scaling heads. The proposed method demonstrates the advanced performance in both the quality of our synthesized results under a severely misaligned and unpaired data setting, and better stability than other GAN-based algorithms. The proposed method also reduces the demand for deformable registration while encouraging to leverage the misaligned and unpaired data. Experimental results verify the outstanding performance of our learning paradigm compared to other state-of-the-art approaches.
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。
