Area of research
Computational Mechanics · Computer Vision and Pattern Recognition
Research interest
Research interests include Computer science, Traffic flow (computer networking), Feature (linguistics), Convolution (computer science), and Flow (mathematics).
MSMTSeg: Multi-Stained Multi-Tissue Segmentation of Kidney Histology Images via Generative Self-Supervised Meta-Learning Framework
Classification and quantification of glomerular spike-like projections via deep residual multiple instance learning with multi-scale annotation
Diagnosis of diabetic kidney disease in whole slide images via AI-driven quantification of pathological indicators
Computed Tomography–Based Deep Learning Model for Assessing the Severity of Patients With Connective Tissue Disease–Associated Interstitial Lung Disease
Interstitial fibrosis and tubular atrophy measurement via hierarchical extractions of kidney and atrophy regions with deep learning method
Multi‐instance inflated 3D CNN for classifying urine red blood cells from multi‐focus videos
Quantitative assessment of interstitial lung disease based on RDNet convolutional network
Classification of renal biopsy direct immunofluorescence image using multiple attention convolutional neural network
Application of Lightweight Convolution Neural Network in Cancer Diagnosis
Multi-step traffic flow prediction method based on the Conv1D + LSTM