157 citations · 246 across the 15 of their papers we have counts for
15 papers
GraphMAR: Geometry-Aware Graph Learning Framework for Spatially Adaptive CT Metal Artifact Reduction
Zilong Li, Chenglong Ma, Yiming Lei +7
Computed tomography (CT) metal artifact reduction (MAR) aims to reduce the severe streaking artifacts induced by metallic implants and other high-density objects. Effective MAR gen…
PHASOR: Anatomy- and Phase-Consistent Volumetric Diffusion for CT Virtual Contrast Enhancement
Zilong Li, Dongyang Li, Chenglong Ma +6
Contrast-enhanced computed tomography (CECT) is pivotal for highlighting tissue perfusion and vascularity, yet its clinical ubiquity is impeded by the invasive nature of contrast a…
FoundDiff: Foundational Diffusion Model for Generalizable Low-Dose CT Denoising
Zhihao Chen, Qi Gao, Zilong Li +4
Low-dose computed tomography (CT) denoising is crucial for reduced radiation exposure while ensuring diagnostically acceptable image quality. Despite significant advancements drive…
Unified Medical Image Tokenizer for Autoregressive Synthesis and Understanding
Chenglong Ma, Yuanfeng Ji, Jin Ye +9
Autoregressive modeling has driven major advances in multimodal AI, yet its application to medical imaging remains constrained by the absence of a unified image tokenizer that simu…
Radiologist-in-the-Loop Self-Training for Generalizable CT Metal Artifact Reduction
Chenglong Ma, Zilong Li, Yuanlin Li +5
Metal artifacts in computed tomography (CT) images can significantly degrade image quality and impede accurate diagnosis. Supervised metal artifact reduction (MAR) methods, trained…
Prompt-In-Prompt Learning for Universal Image Restoration
Zilong Li, Yiming Lei, Chenglong Ma +2
Image restoration, which aims to retrieve and enhance degraded images, is fundamental across a wide range of applications. While conventional deep learning approaches have notably…
Prompted Contextual Transformer for Incomplete-View CT Reconstruction
Chenglong Ma, Zilong Li, Junjun He +3
Incomplete-view computed tomography (CT) can shorten the data acquisition time and allow scanning of large objects, including sparse-view and limited-angle scenarios, each with var…
LICO: Explainable Models with Language-Image Consistency
Yiming Lei, Zilong Li, Yangyang Li +2
Interpreting the decisions of deep learning models has been actively studied since the explosion of deep neural networks. One of the most convincing interpretation approaches is sa…
Learning to Distill Global Representation for Sparse-View CT
Zilong Li, Chenglong Ma, Jie Chen +2
Sparse-view computed tomography (CT) -- using a small number of projections for tomographic reconstruction -- enables much lower radiation dose to patients and accelerated data acq…
FreeSeed: Frequency-band-aware and Self-guided Network for Sparse-view CT Reconstruction
Chenglong Ma, Zilong Li, Junping Zhang +2
Sparse-view computed tomography (CT) is a promising solution for expediting the scanning process and mitigating radiation exposure to patients, the reconstructed images, however, c…
CLIP-Lung: Textual Knowledge-Guided Lung Nodule Malignancy Prediction
Yiming Lei, Zilong Li, Yan Shen +2
Lung nodule malignancy prediction has been enhanced by advanced deep-learning techniques and effective tricks. Nevertheless, current methods are mainly trained with cross-entropy l…
CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization
Qi Gao, Zilong Li, Junping Zhang +2
Low-dose computed tomography (CT) images suffer from noise and artifacts due to photon starvation and electronic noise. Recently, some works have attempted to use diffusion models…
CORE: Learning Consistent Ordinal REpresentations for Image Ordinal Estimation
Yiming Lei, Zilong Li, Yangyang Li +2
The goal of image ordinal estimation is to estimate the ordinal label of a given image with a convolutional neural network. Existing methods are mainly based on ordinal regression…
From Node Interaction to Hop Interaction: New Effective and Scalable Graph Learning Paradigm
Jie Chen, Zilong Li, Yin Zhu +2
Existing Graph Neural Networks (GNNs) follow the message-passing mechanism that conducts information interaction among nodes iteratively. While considerable progress has been made,…
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…