Hai Shu

Orcid: 0000-0002-6968-4063

According to our database1, Hai Shu authored at least 25 papers between 2018 and 2024.

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

Timeline

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Bibliography

2024
Multi-Scale Tokens-Aware Transformer Network for Multi-Region and Multi-Sequence MR-to-CT Synthesis in a Single Model.
IEEE Trans. Medical Imaging, February, 2024

3D U-KAN Implementation for Multi-modal MRI Brain Tumor Segmentation.
CoRR, 2024

D-CDLF: Decomposition of Common and Distinctive Latent Factors for Multi-view High-dimensional Data.
CoRR, 2024

DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
A generic fundus image enhancement network boosted by frequency self-supervised representation learning.
Medical Image Anal., December, 2023

Cross-Task Feedback Fusion GAN for Joint MR-CT Synthesis and Segmentation of Target and Organs-at-Risk.
IEEE Trans. Artif. Intell., October, 2023

United multi-task learning for abdominal contrast-enhanced CT synthesis through joint deformable registration.
Comput. Methods Programs Biomed., April, 2023

QACL: Quartet attention aware closed-loop learning for abdominal MR-to-CT synthesis via simultaneous registration.
Medical Image Anal., 2023

K-Nearest-Neighbor Local Sampling Based Conditional Independence Testing.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Domain Adaptative Retinal Image Quality Assessment with Knowledge Distillation Using Competitive Teacher-Student Network.
Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023

Self-Supervision Boosted Retinal Vessel Segmentation for Cross-Domain Data.
Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023

2022
D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data.
J. Mach. Learn. Res., 2022

A Comparative Study of non-deep Learning, Deep Learning, and Ensemble Learning Methods for Sunspot Number Prediction.
Appl. Artif. Intell., 2022

Structure-Consistent Restoration Network for Cataract Fundus Image Enhancement.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022

mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural Networks.
Proceedings of the International Joint Conference on Neural Networks, 2022

2021
BiTr-Unet: A CNN-Transformer Combined Network for MRI Brain Tumor Segmentation.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2021

2020
(TS)<sup>2</sup>WM: Tumor Segmentation and Tract Statistics for Assessing White Matter Integrity with Applications to Glioblastoma Patients.
NeuroImage, 2020

Adversarial Image Generation and Training for Deep Convolutional Neural Networks.
CoRR, 2020

D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multiple High-dimensional Datasets.
CoRR, 2020

Variational-Autoencoder Regularized 3D MultiResUNet for the BraTS 2020 Brain Tumor Segmentation.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2020

A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2020

2019
CDPA: Common and Distinctive Pattern Analysis between High-dimensional Datasets.
CoRR, 2019

Sensitivity Analysis of Deep Neural Networks.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Automatic Brain Tumor Segmentation with Domain Adaptation.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2018

A label-fusion-aided convolutional neural network for isointense infant brain tissue segmentation.
Proceedings of the 15th IEEE International Symposium on Biomedical Imaging, 2018


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