Search Results for author:
Found 12 papers, 2 papers with code
Dual Graph Attention based Disentanglement Multiple Instance Learning for Brain Age Estimation
To overcome these limitations, we propose a Dual Graph Attention based Disentanglement Multi-instance Learning (DGA-DMIL) framework for improving brain age estimation.
Data Augmentation for Seizure Prediction with Generative Diffusion Model
Data augmentation is an intuitive way to solve this problem.
PanFlowNet: A Flow-Based Deep Network for Pan-sharpening
The experimental results verify that the proposed PanFlowNet can generate various HRMS images given an LRMS image and a PAN image.
Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction
Interpretable neural network models are of significant interest since they enhance the trustworthiness required in clinical practice when dealing with medical images.
Memory-Augmented Deep Conditional Unfolding Network for Pan-Sharpening
Pan-sharpening aims to obtain high-resolution multispectral (MS) images for remote sensing systems and deep learning-based methods have achieved remarkable success.
Automated assessment of disease severity of COVID-19 using artificial intelligence with synthetic chest CT
Correlations between imaging findings and clinical lab tests suggested the value of this system as a potential tool to assess disease severity of COVID-19.
Toward Open-World Electroencephalogram Decoding Via Deep Learning: A Comprehensive Survey
Electroencephalogram (EEG) decoding aims to identify the perceptual, semantic, and cognitive content of neural processing based on non-invasively measured brain activity.
Unfolding Taylor's Approximations for Image Restoration
Deep learning provides a new avenue for image restoration, which demands a delicate balance between fine-grained details and high-level contextualized information during recovering the latent clear image.
Image De-Raining via Continual Learning
The proposed model is capable of achieving superior performance on both inhomogeneous and incremental datasets, and is promising for highly compact systems to gradually learn myriad regularities of the different types of rain streaks.
Improving De-Raining Generalization via Neural Reorganization
Equipped with our NR algorithm, the deep model can be trained on a list of synthetic rainy datasets by overcoming catastrophic forgetting, making it a general-version de-raining network.
Learning Dual Priors for JPEG Compression Artifacts Removal
Specifically, we design a variational model to formulate the image de-blocking problem and propose two prior terms for the image content and gradient, respectively.
MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG
MLBF-Net is composed of three components: 1) multiple lead-specific branches for learning the diversity of multi-lead ECG; 2) cross-lead features fusion by concatenating the output feature maps of all branches for learning the integrity of multi-lead ECG; 3) multi-loss co-optimization for all the individual branches and the concatenated network.
