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
Using the Order of Tomographic Slices as a Prior for Neural Networks Pre-Training
Yaroslav Zharov, Alexey Ershov, Tilo Baumbach, Vincent Heuveline
Simulation-Driven Training of Vision Transformers Enabling Metal Segmentation in X-Ray Images
Fuxin Fan, Ludwig Ritschl, Marcel Beister, Ramyar Biniazan, Björn Kreher, Tristan M. Gottschalk, Steffen Kappler, Andreas Maier
Automated processing of X-ray computed tomography images via panoptic segmentation for modeling woven composite textiles
Aaron Allred, Lauren J. Abbott, Alireza Doostan, Kurt Maute
MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray
Abril Corona-Figueroa, Jonathan Frawley, Sam Bond-Taylor, Sarath Bethapudi, Hubert P. H. Shum, Chris G. Willcocks
Debiasing pipeline improves deep learning model generalization for X-ray based lung nodule detection
Michael Horry, Subrata Chakraborty, Biswajeet Pradhan, Manoranjan Paul, Jing Zhu, Hui Wen Loh, Prabal Datta Barua, U. Rajendra Arharya
Improving Chest X-Ray Report Generation by Leveraging Warm Starting
Aaron Nicolson, Jason Dowling, Bevan Koopman