Shuai Wang

Orcid: 0000-0001-8897-9476

Affiliations:
  • School of Medicine, Tsinghua University, Beijing, China


According to our database1, Shuai Wang authored at least 13 papers between 2022 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Bibliography

2025
Multi-contrast image super-resolution with deformable attention and neighborhood-based feature aggregation (DANCE): Applications in anatomic and metabolic MRI.
Medical Image Anal., 2025

2024
COMET: Cross-Space Optimization-Based Mutual Learning Network for Super-Resolution of CEST-MRI.
IEEE J. Biomed. Health Informatics, January, 2024

Generalized Robust Fundus Photography-Based Vision Loss Estimation for High Myopia.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2024, 2024

2023
Towards Generalizable Medical Image Segmentation with Pixel-wise Uncertainty Estimation.
CoRR, 2023

Black-box Source-free Domain Adaptation via Two-stage Knowledge Distillation.
CoRR, 2023

DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion models.
CoRR, 2023

Bootstrap The Original Latent: Learning a Private Model from a Black-box Model.
CoRR, 2023

VF-HM: Vision Loss Estimation Using Fundus Photograph for High Myopia.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

Teaching What You Should Teach: A Data-Based Distillation Method.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Prototype Knowledge Distillation for Medical Segmentation with Missing Modality.
Proceedings of the IEEE International Conference on Acoustics, 2023

Feature Alignment and Uniformity for Test Time Adaptation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Undersampled Multi-Contrast MRI Reconstruction Based on Double-Domain Generative Adversarial Network.
IEEE J. Biomed. Health Informatics, 2022

Learning What You Should Learn.
CoRR, 2022


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