Heading Towards Robust Solutions

With the rapid development of information technology in the field of medicine, the acquisition and sharing of medical data show a significant growth trend. However, medical data involves sensitive information such as patients' clinical records, diagnostic results, and medical images, making data security and reasonable sharing a pressing challenge. Despite some progress in research on medical data sharing, the complexity and diversity of the issues persist. Various stakeholders in medical data, including medical institutions, researchers, and patients, have different expectations for data privacy protection, adding to the complexity of the challenge. Against this background, this paper focuses on the application of anomaly detection technology and proposes the GAN-Transformer model. This model cleverly combines Generative Adversarial Networks (GAN) with Transformer networks, creating a powerful and balanced framework for anomaly detection.