Search Results for author:
Found 11 papers, 7 papers with code
The stability and instability of the language control network: a longitudinal resting-state functional magnetic resonance imaging study
These findings provide preliminary evidence of the coexistence of stability and instability in the language control network.
Prompted Contextual Transformer for Incomplete-View CT Reconstruction
To enjoy the multi-setting synergy in a single model, we propose a novel Prompted Contextual Transformer (ProCT) for incomplete-view CT reconstruction.
Prompt-In-Prompt Learning for Universal Image Restoration
Second, we devise a novel prompt-to-prompt interaction module to fuse these two prompts into a universal restoration prompt.
Learning to Distill Global Representation for Sparse-View CT
The reconstructed images, however, suffer from strong artifacts, greatly limiting their diagnostic value.
FreeSeed: Frequency-band-aware and Self-guided Network for Sparse-view CT Reconstruction
Specifically, we first propose a frequency-band-aware artifact modeling network (FreeNet), which learns artifact-related frequency-band attention in Fourier domain for better modeling the globally distributed streak artifact on the sparse-view CT images.
CLIP-Lung: Textual Knowledge-Guided Lung Nodule Malignancy Prediction
Drawing on the capability of the contrastive language-image pre-training (CLIP) model to learn generalized visual representations from text annotations, in this paper, we propose CLIP-Lung, a textual knowledge-guided framework for lung nodule malignancy prediction.
CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization
First, CoreDiff utilizes LDCT images to displace the random Gaussian noise and employs a novel mean-preserving degradation operator to mimic the physical process of CT degradation, significantly reducing sampling steps thanks to the informative LDCT images as the starting point of the sampling process.
CORE: Learning Consistent Ordinal REpresentations for Image Ordinal Estimation
However, the manifold of the resultant feature representations does not maintain the intrinsic ordinal relations of interest, which hinders the effectiveness of the image ordinal estimation.
From Node Interaction to Hop Interaction: New Effective and Scalable Graph Learning Paradigm
We design a simple yet effective HopGNN framework that can easily utilize existing GNNs to achieve hop interaction.
Quad-Net: Quad-domain Network for CT Metal Artifact Reduction
Here we extend the state-of-the-art dual-domain deep network approach into a quad-domain counterpart so that all the features in the sinogram, image, and their corresponding Fourier domains are synergized to eliminate metal artifacts optimally without compromising structural subtleties.
Lagrange Motion Analysis and View Embeddings for Improved Gait Recognition
Gait is considered the walking pattern of human body, which includes both shape and motion cues.
