X Ray
X-ray technology is fundamental to various scientific fields, with current research heavily focused on improving image analysis and interpretation through advanced computational methods. This involves developing and applying deep learning models, including convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), and diffusion models, to automate tasks such as image reconstruction, object detection (e.g., fractures, catheters, lung abnormalities), and report generation. These advancements significantly impact healthcare by enabling faster, more accurate diagnoses and treatment planning, while also enhancing materials science and other fields through improved data analysis and characterization techniques.
Papers
Enrichment of the NLST and NSCLC-Radiomics computed tomography collections with AI-derived annotations
Deepa Krishnaswamy, Dennis Bontempi, Vamsi Thiriveedhi, Davide Punzo, David Clunie, Christopher P Bridge, Hugo JWL Aerts, Ron Kikinis, Andrey Fedorov
XTransCT: Ultra-Fast Volumetric CT Reconstruction using Two Orthogonal X-Ray Projections for Image-guided Radiation Therapy via a Transformer Network
Chulong Zhang, Lin Liu, Jingjing Dai, Xuan Liu, Wenfeng He, Yinping Chan, Yaoqin Xie, Feng Chi, Xiaokun Liang