Area of research
Biophysics · Media Technology
Research interest
Research focused on Artificial intelligence and Infrared, with related work in Fusion, Convolutional neural network, Spectroscopy. Notable publications include 'AF-SENet: Classification of Cancer in Cervical Tissue Pathological Images Based on Fusing Deep Convolution Features', 'DSFusion: Infrared and visible image fusion method combining detail and scene information', and 'ASFFuse: Infrared and visible image fusion model based on adaptive selection feature maps'.
Rethinking link prediction: A multi-scale graph masked autoencoder
Rapid diagnosis of celiac disease based on serum infrared spectroscopy combined with deep learning
DSFusion: Infrared and visible image fusion method combining detail and scene information
ASFFuse: Infrared and visible image fusion model based on adaptive selection feature maps
DCFusion: Difference correlation-driven fusion mechanism of infrared and visible images
Efficient time series adaptive representation learning via Dynamic Routing Sparse Attention
SeACPFusion: An Adaptive Fusion Network for Infrared and Visible Images based on brightness perception
Diagnosing the degree of differentiation between types of oral cancer based on extreme deep neural network model and Raman spectroscopy
Application of one-dimensional hierarchical network assisted screening for cervical cancer based on Raman spectroscopy combined with attention mechanism
Self-immunological disease aid diagnosis with ConvSANet and Eu-clidean distance
Bi-Branching Feature Interaction Representation Learning for Multivariate Time Series
Rapid diagnosis of osteoarthritis using serum Raman spectroscopy based on MFCC transformation combined with MCNN
Rethinking the Necessity of Learnable Modal Alignment for Medical Image Fusion
Rapid diagnosis of rheumatoid arthritis and ankylosing spondylitis based on Fourier transform infrared spectroscopy and deep learning
Application of serum mid-infrared spectroscopy combined with an ensemble learning method in rapid diagnosis of gliomas
AF-SENet: Classification of Cancer in Cervical Tissue Pathological Images Based on Fusing Deep Convolution Features