A label-fusion-aided convolutional neural network for isointense infant brain tissue segmentation. - Tengfei Li 001; Fan Zhou 003; Ziliang Zhu; Hai Shu; Hongtu Zhu (ISBI, 2018)

Automatic Brain Tumor Segmentation with Domain Adaptation. - Lutao Dai; Tengfei Li 001; Hai Shu; Liming Zhong; Haipeng Shen; Hongtu Zhu (BrainLes@MICCAI (2), 2018)

Sensitivity Analysis of Deep Neural Networks. - Hai Shu; Hongtu Zhu (AAAI, 2019)

CDPA: Common and Distinctive Pattern Analysis between High-dimensional Datasets. - Zhe Qu; Hai Shu (CoRR, 2019)

A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation. - Chenggang Lyu; Hai Shu (BrainLes@MICCAI (1), 2020)

Variational-Autoencoder Regularized 3D MultiResUNet for the BraTS 2020 Brain Tumor Segmentation. - Jiarui Tang; Tengfei Li 001; Hai Shu; Hongtu Zhu (BrainLes@MICCAI (2), 2020)

D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multiple High-dimensional Datasets. - Hai Shu; Zhe Qu; Hongtu Zhu (CoRR, 2020)

Adversarial Image Generation and Training for Deep Convolutional Neural Networks. - Ronghua Shi; Hai Shu; Hongtu Zhu; Ziqi Chen 002 (CoRR, 2020)

(TS)2WM: Tumor Segmentation and Tract Statistics for Assessing White Matter Integrity with Applications to Glioblastoma Patients. - Liming Zhong; Tengfei Li 001; Hai Shu; Chao Huang 005; Jason M. Johnson; Donald F. Schomer; Ho-Ling Liu; Qianjin Feng; Wei Yang 006; Hongtu Zhu (NeuroImage, 2020)

BiTr-Unet: A CNN-Transformer Combined Network for MRI Brain Tumor Segmentation. - Qiran Jia; Hai Shu (BrainLes@MICCAI (2), 2021)

mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural Networks. - Hai Shu; Ronghua Shi; Qiran Jia; Hongtu Zhu; Ziqi Chen 002 (IJCNN, 2022)

Structure-Consistent Restoration Network for Cataract Fundus Image Enhancement. - Heng Li 010; Haofeng Liu; Huazhu Fu; Hai Shu; Yitian Zhao; Xiaoling Luo; Yan Hu; Jiang Liu 001 (MICCAI (2), 2022)

A Comparative Study of non-deep Learning, Deep Learning, and Ensemble Learning Methods for Sunspot Number Prediction. - Yuchen Dang; Ziqi Chen 002; Heng Li 010; Hai Shu (Appl. Artif. Intell., 2022)

D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data. - Hai Shu; Zhe Qu; Hongtu Zhu (J. Mach. Learn. Res., 2022)

Self-Supervision Boosted Retinal Vessel Segmentation for Cross-Domain Data. - Haojin Li; Heng Li 010; Hai Shu; Jianyu Chen; Yan Hu; Jiang Liu 001 (ISBI, 2023)

Domain Adaptative Retinal Image Quality Assessment with Knowledge Distillation Using Competitive Teacher-Student Network. - Yuanming Lin; Heng Li 010; Haofeng Liu; Hai Shu; Zinan Li; Yan Hu; Jiang Liu 001 (ISBI, 2023)

K-Nearest-Neighbor Local Sampling Based Conditional Independence Testing. - Shuai Li 020; Yingjie Zhang 004; Hongtu Zhu; Christina Dan Wang; Hai Shu; Ziqi Chen 002; Zhuoran Sun; Yanfeng Yang (NeurIPS, 2023)

QACL: Quartet attention aware closed-loop learning for abdominal MR-to-CT synthesis via simultaneous registration. - Liming Zhong; Zeli Chen; Hai Shu; Yikai Zheng; Yiwen Zhang; Yuankui Wu; Qianjin Feng; Yin Li; Wei Yang 006 (Medical Image Anal., 2023)

United multi-task learning for abdominal contrast-enhanced CT synthesis through joint deformable registration. - Liming Zhong; Pinyu Huang; Hai Shu; Yin Li; Yiwen Zhang; Qianjin Feng; Yuankui Wu; Wei Yang 006 (Comput. Methods Programs Biomed., 2023)

Cross-Task Feedback Fusion GAN for Joint MR-CT Synthesis and Segmentation of Target and Organs-at-Risk. - Yiwen Zhang; Liming Zhong; Hai Shu; Zhenhui Dai; Kaiyi Zheng; Zefeiyun Chen; Qianjin Feng; Xuetao Wang; Wei Yang 006 (IEEE Trans. Artif. Intell., 2023)

A generic fundus image enhancement network boosted by frequency self-supervised representation learning. - Heng Li 010; Haofeng Liu; Huazhu Fu; Yanwu Xu 001; Hai Shu; Ke Niu 002; Yan Hu; Jiang Liu 001 (Medical Image Anal., 2023)

DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data. - Taehyo Kim; Hai Shu; Qiran Jia; Mony J. de Leon (AISTATS, 2024)

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

3D U-KAN Implementation for Multi-modal MRI Brain Tumor Segmentation. - Tianze Tang; Yanbing Chen; Hai Shu (CoRR, 2024)

Multi-Scale Tokens-Aware Transformer Network for Multi-Region and Multi-Sequence MR-to-CT Synthesis in a Single Model. - Liming Zhong; Zeli Chen; Hai Shu; Kaiyi Zheng; Yin Li; Weicui Chen; Yuankui Wu; Jianhua Ma 001; Qianjin Feng; Wei Yang 006 (IEEE Trans. Medical Imaging, 2024)

Conditional Diffusion Models Based Conditional Independence Testing. - Yanfeng Yang; Shuai Li 020; Yingjie Zhang 004; Zhuoran Sun; Hai Shu; Ziqi Chen 002; Renming Zhang (AAAI, 2025)

Enhancing missing data imputation through combined bipartite graph and complete directed graph. - Zhaoyang Zhang; Hongtu Zhu; Yingjie Zhang 004; Hai Shu; Ziqi Chen 002 (Neurocomputing, 2025)

NCCT-to-CECT synthesis with contrast-enhanced knowledge and anatomical perception for multi-organ segmentation in non-contrast CT images. - Liming Zhong; Ruolin Xiao; Hai Shu; Kaiyi Zheng; Xinming Li; Yuankui Wu; Jianhua Ma 001; Qianjin Feng; Wei Yang 006 (Medical Image Anal., 2025)

Nonlinear Sparse Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data. - Rong Wu; Ziqi Chen 002; Gen Li; Hai Shu (CoRR, 2025)

A False Discovery Rate Control Method Using a Fully Connected Hidden Markov Random Field for Neuroimaging Data. - Taehyo Kim; Qiran Jia; Mony J. de Leon; Hai Shu (CoRR, 2025)

Unleashing Diffusion and State Space Models for Medical Image Segmentation. - Rong Wu; Ziqi Chen 002; Liming Zhong; Heng Li 010; Hai Shu (CoRR, 2025)