Area of research
Artificial Intelligence · Computer Vision and Pattern Recognition
Research interest
Research interests include Domain Adaptation and Few-Shot Learning, AI in cancer detection, Multimodal Machine Learning Applications, and COVID-19 diagnosis using AI.
The RsWRKY18-RsHSFA2 module confers thermotolerance by activating <i>RsHSP22</i> during taproot thickening in radish
Semi-supervised medical image segmentation via weak-to-strong perturbation consistency and edge-aware contrastive representation
Replacing cholesterol and PEGylated lipids with zwitterionic ionizable lipids in LNPs for spleen-specific mRNA translation
RsKNAT3 Interacts Antagonistically With RsKNAT1 to Confer Thermotolerance by Regulating <i>RsDREB2A</i> Transcription in Radish
Adnexal Lesion Discrimination Using Deep Learning Analysis of Dynamic Contrast-enhanced US Images
Visual Class Incremental Learning With Textual Priors Guidance Based on an Adapted Vision-Language Model
Biochanin A Mitigates Pressure Overload-Induced Cardiac Hypertrophy Through Modulation of the NF-κB/Cbl-b/NLRP3 Signaling Axis
Super-enhancers and Mef2c: Novel regulators of cardiac hypertrophy via the Hey2/Notch/p38 signaling pathway
Boosting confidence in class-incremental learning via discriminative local feature enhancement
Automatic Lenke classification of adolescent idiopathic scoliosis with deep learning
Early detection of visual impairment in young children using a smartphone-based deep learning system
SATS: Self-attention transfer for continual semantic segmentation
PCCT: Progressive Class-Center Triplet Loss for Imbalanced Medical Image Classification
Continual learning with Bayesian model based on a fixed pre-trained feature extractor
Class attention to regions of lesion for imbalanced medical image recognition
Adapter Learning in Pretrained Feature Extractor for Continual Learning of Diseases
Task-Incremental Medical Image Classification with Task-Specific Batch Normalization
Pooling in convolutional neural networks for medical image analysis: a survey and an empirical study
Skin Cancer Classification With Deep Learning: A Systematic Review
A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology
A digital mask to safeguard patient privacy
Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images
Ensembled deep learning model outperforms human experts in diagnosing biliary atresia from sonographic gallbladder images
Fusing Metadata and Dermoscopy Images for Skin Disease Diagnosis
Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images