Restoration of X-ray phase-contrast imaging based on generative adversarial networks.

For light-element materials, X-ray phase contrast imaging provides better contrast compared to absorption imaging. While the Fourier transform method has a shorter imaging time, it typically results in lower image quality; in contrast, the phase-shifting method offers higher image quality but is more time-consuming and involves a higher radiation dose. To rapidly reconstruct low-dose X-ray phase contrast images, this study developed a model based on Generative Adversarial Networks (GAN), incorporating custom layers and self-attention mechanisms to recover high-quality phase contrast images.

Mpox-AISM: AI-mediated super monitoring for mpox and like-mpox.

Swift and accurate diagnosis for earlier-stage monkeypox (mpox) patients is crucial to avoiding its spread. However, the similarities between common skin disorders and mpox and the need for professional diagnosis unavoidably impaired the diagnosis of earlier-stage mpox patients and contributed to mpox outbreak. To address the challenge, we proposed "Super Monitoring", a real-time visualization technique employing artificial intelligence (AI) and Internet technology to diagnose earlier-stage mpox cheaply, conveniently, and quickly.