356 citations · 799 across the 42 of their papers we have counts for
43 papers
Reasoning Language Model for Personalized Lung Cancer Screening
Chuang Niu, Ge Wang
Accurate risk assessment in lung cancer screening is critical for enabling early cancer detection and minimizing unnecessary invasive procedures. The Lung CT Screening Reporting an…
LangMamba: A Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language Models
Zhihao Chen, Tao Chen, Chenhui Wang +5
Low-dose computed tomography (LDCT) reduces radiation exposure but often degrades image quality, potentially compromising diagnostic accuracy. Existing deep learning-based denoisin…
Tomographic Foundation Model -- FORCE: Flow-Oriented Reconstruction Conditioning Engine
Wenjun Xia, Chuang Niu, Ge Wang
Computed tomography (CT) is a major medical imaging modality. Clinical CT scenarios, such as low-dose screening, sparse-view scanning, and metal implants, often lead to severe nois…
Information-Maximized Soft Variable Discretization for Self-Supervised Image Representation Learning
Chuang Niu, Wenjun Xia, Hongming Shan +1
Self-supervised learning (SSL) has emerged as a crucial technique in image processing, encoding, and understanding, especially for developing today's vision foundation models that…
Development and Validation of a Large Language Model for Generating Fully-Structured Radiology Reports
Chuang Niu, Md Sayed Tanveer, Md Zabirul Islam +5
Current LLMs for creating fully-structured reports face the challenges of formatting errors, content hallucinations, and privacy leakage issues when uploading data to external serv…
3D Photon Counting CT Image Super-Resolution Using Conditional Diffusion Model
Chuang Niu, Christopher Wiedeman, Mengzhou Li +2
This study aims to improve photon counting CT (PCCT) image resolution using denoising diffusion probabilistic models (DDPM). Although DDPMs have shown superior performance when app…
CT-based Anomaly Detection of Liver Tumors Using Generative Diffusion Prior
Yongyi Shi, Chuang Niu, Amber L. Simpson +3
CT is a main modality for imaging liver diseases, valuable in detecting and localizing liver tumors. Traditional anomaly detection methods analyze reconstructed images to identify…
Low-dose CT Denoising with Language-engaged Dual-space Alignment
Zhihao Chen, Tao Chen, Chenhui Wang +3
While various deep learning methods were proposed for low-dose computed tomography (CT) denoising, they often suffer from over-smoothing, blurring, and lack of explainability. To a…
Deep Few-view High-resolution Photon-counting CT at Halved Dose for Extremity Imaging
Mengzhou Li, Chuang Niu, Ge Wang +10
X-ray photon-counting computed tomography (PCCT) for extremity allows multi-energy high-resolution (HR) imaging but its radiation dose can be further improved. Despite the great po…
Photon-counting CT using a Conditional Diffusion Model for Super-resolution and Texture-preservation
Christopher Wiedeman, Chuang Niu, Mengzhou Li +3
Ultra-high resolution images are desirable in photon counting CT (PCCT), but resolution is physically limited by interactions such as charge sharing. Deep learning is a possible me…
IQAGPT: Image Quality Assessment with Vision-language and ChatGPT Models
Zhihao Chen, Bin Hu, Chuang Niu +4
Large language models (LLMs), such as ChatGPT, have demonstrated impressive capabilities in various tasks and attracted an increasing interest as a natural language interface acros…
Diffusion Prior Regularized Iterative Reconstruction for Low-dose CT
Wenjun Xia, Yongyi Shi, Chuang Niu +2
Computed tomography (CT) involves a patient's exposure to ionizing radiation. To reduce the radiation dose, we can either lower the X-ray photon count or down-sample projection vie…
Head-Neck Dual-energy CT Contrast Media Reduction Using Diffusion Models
Qing Lyu, Josh Tan, Megan E. Lipford +6
Iodinated contrast media is essential for dual-energy computed tomography (DECT) angiography. Previous studies show that iodinated contrast media may cause side effects, and the in…
Specialty-Oriented Generalist Medical AI for Chest CT Screening
Chuang Niu, Qing Lyu, Christopher D. Carothers +6
Modern medical records include a vast amount of multimodal free text clinical data and imaging data from radiology, cardiology, and digital pathology. Fully mining such big data re…
Translating Radiology Reports into Plain Language using ChatGPT and GPT-4 with Prompt Learning: Promising Results, Limitations, and Potential
Qing Lyu, Josh Tan, Michael E. Zapadka +5
The large language model called ChatGPT has drawn extensively attention because of its human-like expression and reasoning abilities. In this study, we investigate the feasibility…
Parallel Diffusion Model-based Sparse-view Cone-beam Breast CT
Wenjun Xia, Hsin Wu Tseng, Chuang Niu +6
Breast cancer is the most prevalent cancer among women worldwide, and early detection is crucial for reducing its mortality rate and improving quality of life. Dedicated breast com…
X-ray projection imaging of metal oxide particles inside gingival tissues
Jarrod N. Cortez, Ignacio O. Romero, Md Sayed Tanveer +7
There is increasing recognition that oral health affects overall health and systemic diseases. Nonetheless it remains challenging to rapidly screen patient biopsies for signs of in…
QS-ADN: Quasi-Supervised Artifact Disentanglement Network for Low-Dose CT Image Denoising by Local Similarity Among Unpaired Data
Yuhui Ruan, Qiao Yuan, Chuang Niu +4
Deep learning has been successfully applied to low-dose CT (LDCT) image denoising for reducing potential radiation risk. However, the widely reported supervised LDCT denoising netw…
LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and Deblurring
Zhihao Chen, Chuang Niu, Qi Gao +2
This paper studies 3D low-dose computed tomography (CT) imaging. Although various deep learning methods were developed in this context, typically they focus on 2D images and perfor…
Enabling Competitive Performance of Medical Imaging with Diffusion Model-generated Images without Privacy Leakage
Yongyi Shi, Wenjun Xia, Chuang Niu +2
Deep learning methods have impacted almost every research field, demonstrating notable successes in medical imaging tasks such as denoising and super-resolution. However, the prere…
HOME: High-Order Mixed-Moment-based Embedding for Representation Learning
Chuang Niu, Ge Wang
Minimum redundancy among different elements of an embedding in a latent space is a fundamental requirement or major preference in representation learning to capture intrinsic infor…
Quad-Net: Quad-domain Network for CT Metal Artifact Reduction
Zilong Li, Qi Gao, Yaping Wu +5
Metal implants and other high-density objects in patients introduce severe streaking artifacts in CT images, compromising image quality and diagnostic performance. Although various…
Self-Supervised Representation Learning With MUlti-Segmental Informational Coding (MUSIC)
Chuang Niu, Ge Wang
Self-supervised representation learning maps high-dimensional data into a meaningful embedding space, where samples of similar semantic contents are close to each other. Most of th…
Unsupervised Contrastive Learning based Transformer for Lung Nodule Detection
Chuang Niu, Ge Wang
Early detection of lung nodules with computed tomography (CT) is critical for the longer survival of lung cancer patients and better quality of life. Computer-aided detection/diagn…