Self-supervised learning for modal transfer of brain imaging - Cheng, Dapeng; Chen, Chao; Yanyan, Mao; You, Panlu; Huang, Xingdan; Gai, Jiale; Zhao, Feng; Mao, Ning (Frontiers in Neuroscience, 2022)
Today’s brain imaging modality migration techniques are transformed from one modality data in one domain to another. In the specific clinical diagnosis, multiple modal data can be obtained in the same scanning field, and it is more beneficial to synthesize missing modal data by using the diversity characteristics of multiple modal data. Therefore, we introduce a self-supervised learning cycle-consistent generative adversarial network (SLC-GAN) for brain imaging modality transfer. The framework constructs multi-branch input, which enables the framework to learn the diversity characteristics of multi-modal data. In addition, their supervision information is mined from large-scale unsupervised data by establishing auxiliary tasks, and the network is trained by constructing supervision information, which not only ensures the similarity between the input and output of modal images, but also can learn valuable representations for downstream tasks.