Meet ApplyKite Twin, the application assistant that learns with you

Meet your Twin, it learns with you

Your Twin learns with you

Launch week codes:30% off annual Pro20% off monthly Pro

Ends in00h00m00s

Lequan Yu

Assistant Professor

The University of Hong Kong

Hong Kong

Research Interests

Explore related searches

Professors in Medical Imaging

Articles (20)

Year:

2026

Year:

2026

Year:

2026

Year:

2026

Year:

2026

Year:

2026

Year:

2026

Year:

2026

Year:

2026

Year:

2026

Year:

2026

Year:

2026

Year:

2026

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Collaborators (7)

Lei Zhu

Assistant professor

The Hong Kong University of Science and Technology (Guangzhou)

CHINA

View details

Chao Li

Academic, Reader

University of Dundee

UNITED KINGDOM

View details

Yuming Jiang

Assistant Professor

Wake Forest School of Medicine

UNITED STATES

View details

Tingying Peng

Young investigator group leader

Helmholtz Zentrum München Deutsches Forschungszentrum für Umwelt und Gesundheit

GERMANY

View details

Stephanie Kwai Yee Ma

Associate Professor

The University of Hong Kong

HONG KONG

View details

Lei Xing

Professor

Stanford University

UNITED STATES

View details

Hujun Yin

The University of Manchester

UNITED KINGDOM

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Lequan Yu

Research Interests

Explore related searches

Articles (20)

Interpretable Multimodal Cancer Prototyping with Whole Slide Images and Incompletely Paired Genomics

Disentangled Multi-modal Learning of Histology and Transcriptomics for Cancer Characterization

OneEHR: Reproducible and AI Agent-Ready Longitudinal EHR Analysis Toolkit

FADFNet: A fine-tunable and adaptive decomposition-fusion network for cross-dataset low-dose CT and low-dose PET image reconstruction

MorphoNet: Morphological sub-region-based structure learning for WSI analysis

Tackling missing modalities with memory-efficient modality-complementary prompt learning for robust brain tumor segmentation

MCS-Stain: Boosting FFPE-to-HE Virtual Staining With Multiple Cell Semantics

SIB-MIL: Sparsity-Induced Bayesian Neural Network for Robust Multiple Instance Learning on Whole Slide Image Analysis

FineST: contrastive learning integrates histology and spatial transcriptomics for nuclei-resolved ligand-receptor analysis

FM2: Fusing multiple foundation models for pathology image analysis via disentangled consensus-divergence representation

Generative Enhancement for 3D Medical Images

Multi-contrast low-field MRI acceleration with k-space progressive learning and image-space hybrid attention fusion

WAS-Mamba: 3D Medical Image Segmentation via Windowed Attention State Space Model

Knowledge-guided adaptation of pathology foundation models effectively improves cross-domain generalization and demographic fairness

Scaling Chest X-Ray Foundation Models From Mixed Supervisions for Dense Prediction

Democratizing large language model-based graph data augmentation via latent knowledge graphs

Diff-UNet: A diffusion embedded network for robust 3D medical image segmentation

Swin-UMamba†: Adapting Mamba-Based Vision Foundation Models for Medical Image Segmentation

Vivim: A Video Vision Mamba for Ultrasound Video Segmentation

Feature Preserving Shrinkage on Bayesian Neural Networks Via the R2D2 Prior

Collaborators (7)

Lei Zhu

Chao Li

Yuming Jiang

Tingying Peng

Stephanie Kwai Yee Ma

Lei Xing

Hujun Yin

Make your next move.