Search Results for author:
Found 6 papers, 2 papers with code
CheX-GPT: Harnessing Large Language Models for Enhanced Chest X-ray Report Labeling
Moreover, models using expert-annotated data are limited by data scarcity and pre-defined classes, impacting their performance, flexibility and scalability.
CXR-CLIP: Toward Large Scale Chest X-ray Language-Image Pre-training
In this paper, we tackle the lack of image-text data in chest X-ray by expanding image-label pair as image-text pair via general prompt and utilizing multiple images and multiple sections in a radiologic report.
AdaIN-Switchable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising
The secondary auxiliary generator is needed to enforce the cycle-consistency, but the additional memory requirement and increases of the learnable parameters are the main huddles for cycleGAN training.
Unsupervised CT Metal Artifact Learning using Attention-guided beta-CycleGAN
Metal artifact reduction (MAR) is one of the most important research topics in computed tomography (CT).
Deep Learning Interior Tomography for Region-of-Interest Reconstruction
Interior tomography for the region-of-interest (ROI) imaging has advantages of using a small detector and reducing X-ray radiation dose.
Multi-Scale Wavelet Domain Residual Learning for Limited-Angle CT Reconstruction
Limited-angle computed tomography (CT) is often used in clinical applications such as C-arm CT for interventional imaging.
