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
Coarse-Fine View Attention Alignment-Based GAN for CT Reconstruction from Biplanar X-Rays
Zhi Qiao, Hanqiang Ouyang, Dongheng Chu, Huishu Yuan, Xiantong Zhen, Pei Dong, Zhen Qian
Reconstruct Spine CT from Biplanar X-Rays via Diffusion Learning
Zhi Qiao, Xuhui Liu, Xiaopeng Wang, Runkun Liu, Xiantong Zhen, Pei Dong, Zhen Qian
X-ray Made Simple: Radiology Report Generation and Evaluation with Layman's Terms
Kun Zhao, Chenghao Xiao, Chen Tang, Bohao Yang, Kai Ye, Noura Al Moubayed, Liang Zhan, Chenghua Lin
XAMI -- A Benchmark Dataset for Artefact Detection in XMM-Newton Optical Images
Elisabeta-Iulia Dima, Pablo Gómez, Sandor Kruk, Peter Kretschmar, Simon Rosen, Călin-Adrian Popa