No photo of Hai Shu

View Scopus Profile

Hai Shu

Assistant Professor

 https://orcid.org/0000-0002-6968-4063

    • Department of Biostatistics, School of Global Public Health

      New York

      United States

    20152024

    Research activity per year

    Search results

    • 2024

      DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data

      Kim, T., Shu, H., Jia, Q. & de Leon, M. J., 2024, In: Proceedings of Machine Learning Research. 238, p. 946-954 9 p.

      Research output: Contribution to journal › Conference article › peer-review

    • Multi-Scale Tokens-Aware Transformer Network for Multi-Region and Multi-Sequence MR-to-CT Synthesis in a Single Model

      Zhong, L., Chen, Z., Shu, H., Zheng, K., Li, Y., Chen, W., Wu, Y., Ma, J., Feng, Q. & Yang, W., Feb 1 2024, In: IEEE Transactions on Medical Imaging. 43, 2, p. 794-806 13 p.

      Research output: Contribution to journal › Article › peer-review

    • 2023

      A generic fundus image enhancement network boosted by frequency self-supervised representation learning

      Li, H., Liu, H., Fu, H., Xu, Y., Shu, H., Niu, K., Hu, Y. & Liu, J., Dec 2023, In: Medical Image Analysis. 90, 102945.

      Research output: Contribution to journal › Article › peer-review

      Open Access

    • Cross-Task Feedback Fusion GAN for Joint MR-CT Synthesis and Segmentation of Target and Organs-at-Risk

      Zhang, Y., Zhong, L., Shu, H., Dai, Z., Zheng, K., Chen, Z., Feng, Q., Wang, X. & Yang, W., Oct 1 2023, In: IEEE Transactions on Artificial Intelligence. 4, 5, p. 1246-1257 12 p.

      Research output: Contribution to journal › Article › peer-review

    • Domain Adaptative Retinal Image Quality Assessment with Knowledge Distillation Using Competitive Teacher-Student Network

      Lin, Y., Li, H., Liu, H., Shu, H., Li, Z., Hu, Y. & Liu, J., 2023, 2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023. IEEE Computer Society, (Proceedings - International Symposium on Biomedical Imaging; vol. 2023-April).

      Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

    • K-Nearest-Neighbor Local Sampling Based Conditional Independence Testing

      Li, S., Zhang, Y., Zhu, H., Wang, C. D., Shu, H., Chen, Z., Sun, Z. & Yang, Y., 2023, In: Advances in Neural Information Processing Systems. 36

      Research output: Contribution to journal › Conference article › peer-review

    • QACL: Quartet attention aware closed-loop learning for abdominal MR-to-CT synthesis via simultaneous registration

      Zhong, L., Chen, Z., Shu, H., Zheng, Y., Zhang, Y., Wu, Y., Feng, Q., Li, Y. & Yang, W., Jan 2023, In: Medical Image Analysis. 83, 102692.

      Research output: Contribution to journal › Article › peer-review

    • Self-Supervision Boosted Retinal Vessel Segmentation for Cross-Domain Data

      Li, H., Li, H., Shu, H., Chen, J., Hu, Y. & Liu, J., 2023, 2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023. IEEE Computer Society, (Proceedings - International Symposium on Biomedical Imaging; vol. 2023-April).

      Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

    • United multi-task learning for abdominal contrast-enhanced CT synthesis through joint deformable registration

      Zhong, L., Huang, P., Shu, H., Li, Y., Zhang, Y., Feng, Q., Wu, Y. & Yang, W., Apr 2023, In: Computer Methods and Programs in Biomedicine. 231, 107391.

      Research output: Contribution to journal › Article › peer-review

    • 2022

      A Comparative Study of non-deep Learning, Deep Learning, and Ensemble Learning Methods for Sunspot Number Prediction

      Dang, Y., Chen, Z., Li, H. & Shu, H., 2022, In: Applied Artificial Intelligence. 36, 1, 2074129.

      Research output: Contribution to journal › Article › peer-review

      Open Access

    • Big Data and Machine Learning in Oncology

      Wei, P. & Shu, H., 2022, The MD Anderson Manual of Medical Oncology, 4th Edition. McGraw Hill

      Research output: Chapter in Book/Report/Conference proceeding › Chapter

    • BiTr-Unet: A CNN-Transformer Combined Network for MRI Brain Tumor Segmentation

      Jia, Q. & Shu, H., 2022, Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Revised Selected Papers. Crimi, A. & Bakas, S. (eds.). Springer Science and Business Media Deutschland GmbH, p. 3-14 12 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12963 LNCS).

      Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

      Open Access

    • CDPA: Common and distinctive pattern analysis between high-dimensional datasets

      Shu, H. & Qu, Z., 2022, In: Electronic Journal of Statistics. 16, 1, p. 2475-2517 43 p.

      Research output: Contribution to journal › Article › peer-review

      Open Access

    • D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data.

      Shu, H., Qu, Z. & Zhu, H., Jun 1 2022, In: Journal of Machine Learning Research. 23

      Research output: Contribution to journal › Article › peer-review

      Open Access

    • mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural Networks

      Shu, H., Shi, R., Jia, Q., Zhu, H. & Chen, Z., 2022, 2022 International Joint Conference on Neural Networks, IJCNN 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., (Proceedings of the International Joint Conference on Neural Networks; vol. 2022-July).

      Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

      Open Access

    • Structure-Consistent Restoration Network for Cataract Fundus Image Enhancement

      Li, H., Liu, H., Fu, H., Shu, H., Zhao, Y., Luo, X., Hu, Y. & Liu, J., 2022, Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. Wang, L., Dou, Q., Fletcher, P. T., Speidel, S. & Li, S. (eds.). Springer Science and Business Media Deutschland GmbH, p. 487-496 10 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13432 LNCS).

      Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

    • 2021

      A deep learning approach to re-create raw full-field digital mammograms for breast density and texture analysis

      Shu, H., Chiang, T., Wei, P., Do, K. A., Lesslie, M. D., Cohen, E. O., Srinivasan, A., Moseley, T. W., Chang Sen, L. Q., Leung, J. W. T., Dennison, J. B., Hanash, S. M. & Weaver, O. O., 2021, In: Radiology: Artificial Intelligence. 3, 4, e200097.

      Research output: Contribution to journal › Article › peer-review

    • A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation

      Lyu, C. & Shu, H., 2021, Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers. Crimi, A. & Bakas, S. (eds.). Springer Science and Business Media Deutschland GmbH, p. 435-447 13 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12658 LNCS).

      Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

      Open Access

    • Variational-Autoencoder Regularized 3D MultiResUNet for the BraTS 2020 Brain Tumor Segmentation

      Tang, J., Li, T., Shu, H. & Zhu, H., 2021, Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers. Crimi, A. & Bakas, S. (eds.). Springer Science and Business Media Deutschland GmbH, p. 431-440 10 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12659 LNCS).

      Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

    • 2020

      (TS)2WM: Tumor Segmentation and Tract Statistics for Assessing White Matter Integrity with Applications to Glioblastoma Patients

      Zhong, L., Li, T., Shu, H., Huang, C., Michael Johnson, J., Schomer, D. F., Liu, H. L., Feng, Q., Yang, W. & Zhu, H., Dec 2020, In: NeuroImage. 223, 117368.

      Research output: Contribution to journal › Article › peer-review

      Open Access

    • D-CCA: A Decomposition-Based Canonical Correlation Analysis for High-Dimensional Datasets

      Shu, H., Wang, X. & Zhu, H., Jan 2 2020, In: Journal of the American Statistical Association. 115, 529, p. 292-306 15 p.

      Research output: Contribution to journal › Article › peer-review

      Open Access
    • 2019

      Assessment of network module identification across complex diseases

      The DREAM Module Identification Challenge Consortium, Sep 1 2019, In: Nature methods. 16, 9, p. 843-852 10 p.

      Research output: Contribution to journal › Article › peer-review

      Open Access

    • Automatic brain tumor segmentation with domain adaptation

      Dai, L., Li, T., Shu, H., Zhong, L., Shen, H. & Zhu, H., 2019, Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers. Reyes, M., Bakas, S., van Walsum, T., Crimi, A., Keyvan, F. & Kuijf, H. (eds.). Springer Verlag, p. 380-392 13 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11384 LNCS).

      Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

    • Estimation of large covariance and precision matrices from temporally dependent observations

      Shu, H. & Nan, B., Jan 2019, In: Annals of Statistics. 47, 3, p. 1321-1350 30 p.

      Research output: Contribution to journal › Article › peer-review

      Open Access

    • Sensitivity analysis of deep neural networks

      Shu, H. & Zhu, H., 2019, 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019. AAAI press, p. 4943-4950 8 p. (33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019).

      Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

    • 2018

      A label-fusion-aided convolutional neural network for isointense infant brain tissue segmentation

      Li, T., Zhou, F., Zhu, Z., Shu, H. & Zhu, H., May 23 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. IEEE Computer Society, p. 692-695 4 p. (Proceedings - International Symposium on Biomedical Imaging; vol. 2018-April).

      Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

      Open Access

    • Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

      Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R. T., Berger, C., Ha, S. M., Rozycki, M., Prastawa, M., Alberts, E., Lipkova, J., Freymann, J., Kirby, J., Bilello, M., Fathallah-Shaykh, H., Wiest, R., Kirschke, J. & Wiestler, B. & 407 others, Colen, R., Kotrotsou, A., Lamontagne, P., Marcus, D., Milchenko, M., Nazeri, A., Weber, M.-A., Mahajan, A., Baid, U., Gerstner, E., Kwon, D., Acharya, G., Agarwal, M., Alam, M., Albiol, A., Albiol, A., Albiol, F. J., Alex, V., Allinson, N., Amorim, P. H. A., Amrutkar, A., Anand, G., Andermatt, S., Arbel, T., Arbelaez, P., Avery, A., Azmat, M., Pranjal, B., Bai, W., Banerjee, S., Barth, B., Batchelder, T., Batmanghelich, K., Battistella, E., Beers, A., Belyaev, M., Bendszus, M., Benson, E., Bernal, J., Bharath, H. N., Biros, G., Bisdas, S., Brown, J., Cabezas, M., Cao, S., Cardoso, J. M., Carver, E. N., Casamitjana, A., Castillo, L. S., Catà, M., Cattin, P., Cerigues, A., Chagas, V. S., Chandra, S., Chang, Y.-J., Chang, S., Chang, K., Chazalon, J., Chen, S., Chen, W., Chen, J. W., Chen, Z., Cheng, K., Choudhury, A. R., Chylla, R., Clérigues, A., Colleman, S., Colmeiro, R. G. R., Combalia, M., Costa, A., Cui, X., Dai, Z., Dai, L., Daza, L. A., Deutsch, E., Ding, C., Dong, C., Dong, S., Dudzik, W., Eaton-Rosen, Z., Egan, G., Escudero, G., Estienne, T., Everson, R., Fabrizio, J., Fan, Y., Fang, L., Feng, X., Ferrante, E., Fidon, L., Fischer, M., French, A. P., Fridman, N., Fu, H., Fuentes, D., Gao, Y., Gates, E., Gering, D., Gholami, A., Gierke, W., Glocker, B., Gong, M., González-Villá, S., Grosges, T., Guan, Y., Guo, S., Gupta, S., Han, W.-S., Han, I. S., Harmuth, K., He, H., Hernández-Sabaté, A., Herrmann, E., Himthani, N., Hsu, W., Hsu, C., Hu, X., Hu, X., Hu, Y., Hu, Y., Hua, R., Huang, T.-Y., Huang, W., Huffel, S. V., Huo, Q., Vivek, H., Iftekharuddin, K. M., Isensee, F., Islam, M., Jackson, A. S., Jambawalikar, S. R., Jesson, A., Jian, W., Jin, P., Jose, V. J. M., Jungo, A., Kainz, B., Kamnitsas, K., Kao, P.-Y., Karnawat, A., Kellermeier, T., Kermi, A., Keutzer, K., Khadir, M. T., Khened, M., Kickingereder, P., Kim, G., King, N., Knapp, H., Knecht, U., Kohli, L., Kong, D., Kong, X., Koppers, S., Kori, A., Krishnamurthi, G., Krivov, E., Kumar, P., Kushibar, K., Lachinov, D., Lambrou, T., Lee, J., Lee, C., Lee, Y., Lee, M., Lefkovits, S., Lefkovits, L., Levitt, J., Li, T., Li, H., Li, W., Li, H., Li, X., Li, Y., Li, H., Li, Z., Li, X., Li, Z., Li, X., Li, W., Lin, Z.-S., Lin, F., Lio, P., Liu, C., Liu, B., Liu, X., Liu, M., Liu, J., Liu, L., Llado, X., Lopez, M. M., Lorenzo, P. R., Lu, Z., Luo, L., Luo, Z., Ma, J., Ma, K., Mackie, T., Madabushi, A., Mahmoudi, I., Maier-Hein, K. H., Maji, P., Mammen, C., Mang, A., Manjunath, B. S., Marcinkiewicz, M., McDonagh, S., McKenna, S., McKinley, R., Mehl, M., Mehta, S., Mehta, R., Meier, R., Meinel, C., Merhof, D., Meyer, C., Miller, R., Mitra, S., Moiyadi, A., Molina-Garcia, D., Monteiro, M. A. B., Mrukwa, G., Myronenko, A., Nalepa, J., Ngo, T., Nie, D., Ning, H., Niu, C., Nuechterlein, N. K., Oermann, E., Oliveira, A., Oliveira, D. D. C., Oliver, A., Osman, A. F. I., Ou, Y.-N., Ourselin, S., Paragios, N., Park, M. S., Paschke, B., Pauloski, J. G., Pawar, K., Pawlowski, N., Pei, L., Peng, S., Pereira, S. M., Perez-Beteta, J., Perez-Garcia, V. M., Pezold, S., Pham, B., Phophalia, A., Piella, G., Pillai, G. N., Piraud, M., Pisov, M., Popli, A., Pound, M. P., Pourreza, R., Prasanna, P., Prkovska, V., Pridmore, T. P., Puch, S., Puybareau, É., Qian, B., Qiao, X., Rajchl, M., Rane, S., Rebsamen, M., Ren, H., Ren, X., Revanuru, K., Rezaei, M., Rippel, O., Rivera, L. C., Robert, C., Rosen, B., Rueckert, D., Safwan, M., Salem, M., Salvi, J., Sanchez, I., Sánchez, I., Santos, H. M., Sartor, E., Schellingerhout, D., Scheufele, K., Scott, M. R., Scussel, A. A., Sedlar, S., Serrano-Rubio, J. P., Shah, N. J., Shah, N., Shaikh, M., Shankar, B. U., Shboul, Z., Shen, H., Shen, D., Shen, L., Shen, H., Shenoy, V., Shi, F., Shin, H. E., Shu, H., Sima, D., Sinclair, M., Smedby, O., Snyder, J. M., Soltaninejad, M., Song, G., Soni, M., Stawiaski, J., Subramanian, S., Sun, L., Sun, R., Sun, J., Sun, K., Sun, Y., Sun, G., Sun, S., Suter, Y. R., Szilagyi, L., Talbar, S., Tao, D., Tao, D., Teng, Z., Thakur, S., Thakur, M. H., Tharakan, S., Tiwari, P., Tochon, G., Tran, T., Tsai, Y. M., Tseng, K.-L., Tuan, T. A., Turlapov, V., Tustison, N., Vakalopoulou, M., Valverde, S., Vanguri, R., Vasiliev, E., Ventura, J., Vera, L., Vercauteren, T., Verrastro, C. A., Vidyaratne, L., Vilaplana, V., Vivekanandan, A., Wang, G., Wang, Q., Wang, C. J., Wang, W., Wang, D., Wang, R., Wang, Y., Wang, C., Wang, G., Wen, N., Wen, X., Weninger, L., Wick, W., Wu, S., Wu, Q., Wu, Y., Xia, Y., Xu, Y., Xu, X., Xu, P., Yang, T.-L., Yang, X., Yang, H.-Y., Yang, J., Yang, H., Yang, G., Yao, H., Ye, X., Yin, C., Young-Moxon, B., Yu, J., Yue, X., Zhang, S., Zhang, A., Zhang, K., Zhang, X., Zhang, L., Zhang, X., Zhang, Y., Zhang, L., Zhang, J., Zhang, X., Zhang, T., Zhao, S., Zhao, Y., Zhao, X., Zhao, L., Zheng, Y., Zhong, L., Zhou, C., Zhou, X., Zhou, F., Zhu, H., Zhu, J., Zhuge, Y., Zong, W., Kalpathy-Cramer, J., Farahani, K., Davatzikos, C., Leemput, K. V. & Menze, B., Nov 5 2018.

      Research output: Working paper › Preprint

      File
    • 2015

      Multiple testing for neuroimaging via hidden Markov random field

      Shu, H., Nan, B. & Koeppe, R., Sep 1 2015, In: Biometrics. 71, 3, p. 741-750 10 p.

      Research output: Contribution to journal › Article › peer-review

      Open Access
    Your message has successfully been sent.
    Your message was not sent due to an error.