17 citations · 24 across the 12 of their papers we have counts for
12 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…
Noise-Inspired Diffusion Model for Generalizable Low-Dose CT Reconstruction
Qi Gao, Zhihao Chen, Dong Zeng +3
The generalization of deep learning-based low-dose computed tomography (CT) reconstruction models to doses unseen in the training data is important and remains challenging. Previou…
PROTOCOL: Partial Optimal Transport-enhanced Contrastive Learning for Imbalanced Multi-view Clustering
Xuqian Xue, Yiming Lei, Qi Cai +2
While contrastive multi-view clustering has achieved remarkable success, it implicitly assumes balanced class distribution. However, real-world multi-view data primarily exhibits c…
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…
DepMamba: Progressive Fusion Mamba for Multimodal Depression Detection
Jiaxin Ye, Junping Zhang, Hongming Shan
Depression is a common mental disorder that affects millions of people worldwide. Although promising, current multimodal methods hinge on aligned or aggregated multimodal fusion, s…
HOPE: Hybrid-granularity Ordinal Prototype Learning for Progression Prediction of Mild Cognitive Impairment
Chenhui Wang, Yiming Lei, Tao Chen +3
Mild cognitive impairment (MCI) is often at high risk of progression to Alzheimer's disease (AD). Existing works to identify the progressive MCI (pMCI) typically require MCI subtyp…
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…
Point, Segment and Count: A Generalized Framework for Object Counting
Zhizhong Huang, Mingliang Dai, Yi Zhang +2
Class-agnostic object counting aims to count all objects in an image with respect to example boxes or class names, \emph{a.k.a} few-shot and zero-shot counting. In this paper, we p…
SIAM: A Simple Alternating Mixer for Video Prediction
Xin Zheng, Ziang Peng, Yuan Cao +2
Video prediction, predicting future frames from the previous ones, has broad applications such as autonomous driving and weather forecasting. Existing state-of-the-art methods typi…