* My student/research assistant
(✉︎) Corresponding author
Co-1: Co-first author
All conference papers are peer-reviewed.
Note: In computer science fields (e.g., deep learning), conferences are as prestigious as journals for publication.
- H. Shu, Z. Chen, Y. Zhang, H. Zhu (2024).
Nodewise Loreg: Nodewise L0-penalized Regression for High-dimensional Sparse Precision Matrix Estimation.
arXiv:2406.06481 - T. Tang *, Y. Chen *, H. Shu (✉︎) (2024).
3D U-KAN Implementation for Multi-modal MRI Brain Tumor Segmentation.
arXiv:2408.00273 - H. Shu, H. Zhu (2024).
Comments on: Data integration via analysis of subspaces (DIVAS).
TEST, DOI: 10.1007/s11749-024-00943-9 - T. Kim *, H. Shu (✉︎, Co-1), Q. Jia *, M. de Leon (2024).
DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data.
International Conference on Artificial Intelligence and Statistics (AISTATS 2024), PMLR 238:946-954. [code][Top conference];
Runner-up Award of the student paper competition for 2024 ASA Statistics in Imaging Section. - L. Zhong, Z. Chen, H. Shu, K. Zheng, Y. Li, W. Chen, Y. Wu, J. Ma, Q.Feng, W. Yang (2023).
Multi-scale Tokens-Aware Transformer Network for Multi-region and Multi-sequence MR-to-CT Synthesis in A Single Model.
IEEE Transactions on Medical Imaging, 43 (2), pp. 794-806. [Top journal] - S. Li, Y. Zhang, H. Zhu, C.D. Wang, H. Shu, Z. Chen, Z. Sun, Y. Yang (2023).
K-Nearest-Neighbor Local Sampling Based Conditional Independence Testing.
Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023). [Top conference] - H. Li, H. Liu, H. Fu, Y. Xu, H. Shu, K. Niu, Y. Hu, J. Liu (2023).
A Generic Fundus Image Enhancement Network Boosted by Frequency Self-supervised Representation Learning.
Medical Image Analysis, 90, 102945, pp. 1-11. [Top journal] - L. Zhong, P. Huang, H. Shu, Y. Li, Y. Zhang, Q. Feng, Y. Wu, W. Yang (2023).
United Multi-task Learning for Abdominal Contrast-enhanced CT Synthesis through Joint Deformable Registration.
Computer Methods and Programs in Biomedicine, 231, 107391, pp. 1-11. [SJR rank: Q1] - H. Li, H. Li, H. Shu, J. Chen, Y. Hu, J. Liu (2023).
Self-Supervision Boosted Retinal Vessel Segmentation for Cross-Domain Data.
Proceedings of 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), DOI: 10.1109/ISBI53787.2023.10230561. [ERA rank: A] - Y. Lin, H. Li, H. Liu, H. Shu, Z. Li, Y. Hu, J. Liu (2023).
Domain Adaptative Retinal Image Quality Assessment with Knowledge Distillation Using Competitive Teacher-Student Network.
Proceedings of 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), DOI: 10.1109/ISBI53787.2023.10230455. [ERA rank: A] - L. Zhong, Z. Chen, H. Shu, Z. Yi, Y. Zhang, Y. Wu, Q. Feng, Y. Li, W. Yang (2023).
QACL: Quartet Attention Aware Closed-loop Learning for Abdominal MR-to-CT Synthesis via Simultaneous Registration.
Medical Image Analysis, 83, 102692, pp. 1-12. [Top journal] - Y. Zhang, L. Zhong, H. Shu, Z. Dai, K. Zheng, Z. Chen, Q. Feng, X. Wang, W. Yang (2023).
Cross-task Feedback Fusion GAN for Joint MR-CT Synthesis and Segmentation of Target and Organs-at-risk.
IEEE Transactions on Artificial Intelligence, 4 (5), pp. 1246-1257. [SJR rank: Q1] - H. Shu (✉︎), Z. Qu, H. Zhu (2022).
D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data.
Journal of Machine Learning Research, 23, 169, pp. 1−64. [code] [Top journal] - H. Li, H. Liu, H. Fu, H. Shu, Y. Zhao, X. Luo, Y. Hu, J. Liu (2022).
Structure-consistent Restoration Network for Cataract Fundus Image Enhancement.
Proceedings of 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), LNCS 13432, pp. 487–496.[pdf] [Top conference] - H. Shu, R. Shi, Q. Jia, H. Zhu, Z. Chen (2022).
mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural Networks.
Proceedings of 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1-8. [pdf][code] [CORE rank: B] - H. Shu (✉︎), Z. Qu (2022).
CDPA: Common and Distinctive Pattern Analysis between High-dimensional Datasets.
Electronic Journal of Statistics, 16 (1), pp. 2475–2517 [code] [SJR rank: Q1] - Y. Dang *, Z. Chen, H. Li, H. Shu (✉︎) (2022).
A Comparative Study of Non-deep learning, Deep learning, and Ensemble Learning Methods for Sunspot Number Prediction.
Applied Artificial Intelligence, 36 (1), e2074129, pp. 900-926. [code] [CORE rank: B] - Q. Jia *, H. Shu (✉︎) (2022).
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor Segmentation.
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, LNCS 12963, pp. 3–14. [pdf][code] - P. Wei, H. Shu (2022).
Big Data and Machine Learning in Oncology.
The MD Anderson Manual of Medical Oncology, 4th Edition, Chapter 70, pp. 1593-1601. McGraw Hill. - H. Shu, T. Chiang, P. Wei, K. Do, M.D. Lesslie, E.O. Cohen, A. Srinivasan, T.W. Moseley, L.Q. Chang-Sen, J.W.T. Leung, J.B. Dennison, S.M. Hanash, O.O. Weaver (2021).
A Deep-learning Approach to Re-create Raw Full-field Digital Mammograms for Breast Density and Texture Analysis.
Radiology: Artificial Intelligence, 3 (4), e200097, pp. 1-11. [SJR rank: Q1] - C. Lyu *, H. Shu (✉︎) (2021).
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation.
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, LNCS 12658, pp. 435-447. [pdf] [code] - J. Tang *, T. Li, H. Shu, H. Zhu (2021).
Variational-Autoencoder Regularized 3D MultiResUNet for the BraTS 2020 Brain Tumor Segmentation.
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, LNCS 12659, pp. 431-440. [pdf] - H. Shu, X. Wang, H. Zhu (2020).
D-CCA: A Decomposition-based Canonical Correlation Analysis for High-dimensional Datasets.
Journal of the American Statistical Association, 115 (529), pp. 292-306 [pdf] [code] [Top journal] - L. Zhong, T. Li, H. Shu, C. Huang, J.M. Johnson, D.F. Schomer, H. Liu, Q. Feng, W. Yang, H. Zhu (2020).
(TS)2WM: Tumor Segmentation and Tract Statistics for Assessing White Matter Integrity with Applications to Glioblastoma Patients.
NeuroImage, 223, 117368, pp. 1-12. [Top journal] - H. Shu, B. Nan (2019).
Estimation of Large Covariance and Precision Matrices from Temporally Dependent Observations.
Annals of Statistics, 47 (3), pp. 1321-1350 [code] [Top journal] - S. Choobdar, et al. [including H. Shu] (2019).
Assessment of Network Module Identification across Complex Diseases.
Nature Methods, 16 (9), pp. 843-852. [Top journal] - H. Shu, H. Zhu (2019).
Sensitivity Analysis of Deep Neural Networks.
Proceedings of The 33rd AAAI Conference on Artificial Intelligence (AAAI), pp. 4943-4950 [slides] [code] [Top conference] - L. Dai, T. Li, H. Shu, L. Zhong, H. Shen, H. Zhu (2019).
Automatic Brain Tumor Segmentation with Domain Adaptation.
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, LNCS 11384, pp. 380-392 [pdf] - S. Bakas, et al. [including H. Shu] (2019).
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge.
Technical report, arXiv:1811.02629 - T. Li, F. Zhou, Z. Zhu, H. Shu, H. Zhu (2018).
A Label-fusion-aided Convolutional Neural Network for Isointense Infant Brain Tissue Segmentation.
Proceedings of 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI), pp. 692-695 [pdf] [ERA rank: A] - H. Shu, B. Nan, R. Koeppe (2015).
Multiple Testing for Neuroimaging via Hidden Markov Random Field.
Biometrics, 71 (3), pp. 741-750 [code] [Top journal] -