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 (BSL-GAN) for brain imaging modality transfer. The framework constructs multi-branch input, which enables the framework to learn the diversity characteristics of multimodal 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 can also learn valuable representations for downstream tasks.
Big data brings tremendous commercial value, but at the same time, it also leads to personal information leakage and other problem. This paper analyses the laws and regulations of the personal information and anonymization at home and abroad, and discusses the legal identification criterion of personal information, personal data and data anonymization systematically. And combining with the practice, this paper provides general methods for anonymizing data with personal privacy when they are used or traded by companies.
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