FedMed-ATL: Misaligned Unpaired Brain Image Synthesis via Affine Transform Loss

01/29/2022
by   Guoyang Xie, et al.
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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 or even luxurious, 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 could not work very well when misaligned data occurs distortions severely, for example, different angles of rotation. 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 advanced performance in both the quality of 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 realize the usage of those misaligned and unpaired data. Experimental results verify the outstanding ability of our learning paradigm compared to other state-of-the-art approaches. Our code is available on the website: https://github.com/FedMed-Meta/FedMed-ATL

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