Area of research
Computer Vision and Pattern Recognition · Neurology
Research interest
Research interests include Computer science, Artificial intelligence, Pattern recognition (psychology), Convolutional neural network, Feature (linguistics), and Computer vision.
Lite-MixedNet: Lightweight and efficient hybrid network for medical image segmentation
DEG-BRIN-GCN: interpretable graph convolutional framework with differentially expressed genes brain region interaction network prior for AD diagnosis
Feature enhanced attention decoder for scene text recognition
CAs-Net: A Channel-Aware Speech Network for Uyghur Speech Recognition
Collaborative Encoding Method for Scene Text Recognition in Low Linguistic Resources: The Uyghur Language Case Study
DHAFormer: Dual-channel hybrid attention network with transformer for polyp segmentation
Dual Feature Enhanced Scene Text Recognition Method for Low-Resource Uyghur
Hybrid Encoding Method for Scene Text Recognition in Low-Resource Uyghur
Correlation-guided decoding strategy for low-resource Uyghur scene text recognition
SEASTR: Spatial-Enhanced Attention Scene Text Recognition
RFE-UNet: Remote Feature Exploration with Local Learning for Medical Image Segmentation
Multi-Branch CNN and Multi-Scale Multi-Dimensional Feature Fusion MLP for Medical Image Classification
Multi-Branch Cnn and Multi-Scale Multi-Dimensional Feature Fusion Mlp for Medical Image Classification
MHANet: A hybrid attention mechanism for retinal diseases classification