Selected Publications

  • Competitive Performance of a Modularized Deep Neural Network Compared to Commercial Algorithms for Low-Dose CT Image Reconstruction
    H. Shan, A. Padole, F. Homayounieh, U. Kruger, R. D. Khera, C. Nitiwarangkul, M. K. Kalra and G. Wang
    Nature Machine Intelligence, 1(6), 269–276, 2019
    [doi][arXiv][pdf][sup][code][Press: NIH/NIBIB, RPI News, EurekAlert!, Physicsworld, HealthImaging, Value Walk, and so on] (ESI highly cited paper, World Artificial Intelligence Conference Youth Outstanding Paper Award)

  • When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework
    Z. Huang, J. Zhang and H. Shan
    In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Online, Jun. 19-25, 2021.
    [arXiv][code](Oral, 4.19% acceptance rate)

  • Framework of Randomized Distribution Features for Visual Representation and Categorization
    H. Shan, J. Zhang and U. Kruger
    IEEE Transactions on Cybernetics, 49(9), 3599-3606, 2019
    [doi]

  • 3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network
    H. Shan, Y. Zhang, Q. Yang, U. Kruger, M. K. Kalra, L. Sun, W. Cong and G. Wang
    IEEE Transactions on Medical Imaging, 37(6), 1522-1534, 2018
    [doi][arXiv][code][Press: Lindau Nobel Laureate Meetings] (ESI highly cited paper)

  • Learning Linear Representation of Space Partitioning Trees based on Unsupervised Kernel Dimension Reduction
    H. Shan, J. Zhang and U. Kruger
    IEEE Transactions on Cybernetics, 46(12), 3427-3438, 2016
    [doi][sup]

  • Synergizing Medical Imaging and Radiotherapy with Deep Learning
    H. Shan*, X. Jia*, P. Yan, Y. Li, H. Paganetti and G. Wang
    Machine Learning: Science and Technology, 1(2), 021001, 2020
    [doi] (* equal contribution)


Book Chapter

  1. Data Augmentation for Training Deep Neural Networks
    Z. Peng, J. Zhou, X. Fang, P. Yan, H. Shan, G. Wang, X. G. Xu and X. Pei
    Auto-Segmentation for Radiation Oncology: State of the Art, Jinzhong Yang, Gregory C. Sharp, Mark J. Gooding (Eds.), CRC Press, 2021
    [doi]

  2. Real-Valued Multivariate Dimension Reduction (in Chinese)
    H. Shan, J. Zhang and W. Xia
    Machine Learning and its Application 2013, Changshui Zhang and Qiang Yang (Eds.), Tsinghua University Press, 2013
    [pdf]


Papers

Preprint

  1. MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising
    Z. Lu, W. Xia, Y. Huang, H. Shan, H. Chen, J. Zhou, Y. Zhang
    [arXiv]

  2. Stabilizing Deep Tomographic Reconstruction Networks
    W. Wu, D. Hu, H. Yu, H. Shan, S. Wang, W. Cong, C. Niu, P. Yan, V. Vardhanabhuti and G. Wang
    [arXiv]

  3. Low-Dimensional Manifold Constrained Disentanglement Network for Metal Artifact Reduction
    C. Niu, W. Cong, F. Fan, H. Shan, M. Li, J. Liang and G. Wang
    [arXiv]

  4. Precipitation Nowcasting with Star-Bridge Networks
    Y. Cao, Q. Li, H. Shan, Z. Zhang, L. Chen, L. Ma and J. Zhang
    [arXiv]

  5. Deep Learning for Accelerating Monte Carlo Radiation Transport Simulation in Intensity-Modulated Radiation Therapy
    Z. Peng, H. Shan, T. Liu, X. Pei, J. Zhou, G. Wang and X. G. Xu
    [arXiv]

  6. A Synergized Pulsing-Imaging Network (SPIN)
    Q. Lyu, T. Xu, H. Shan and G. Wang
    [arXiv]


2021

  1. AgeFlow: Conditional Age Progression and Regression with Normalizing Flows
    Z. Huang, S. Chen, J. Zhang and H. Shan
    In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Online, Aug. 21-26, 2021
    [arXiv](13.9% acceptance rate)

  2. Deep Learning Predicts Cardiovascular Disease Risks from Lung Cancer Screening Low Dose Computed Tomography
    H. Chao, H. Shan, F. Homayounieh, R. Singh, R. D. Khera, H. Guo, T. Su, G. Wang, M. K. Kalra and P. Yan
    Nature Communications, 12, 2963, 2021
    [doi][arXiv][code][Press: DiagnosticImaging, RPI News, EurekAlert!, HealthImaging, and so on]

  3. Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network
    Q. Lyu, H. Shan, Y. Xie, A. Kwan, Y. Otaki, K. Kuronuma, D. Li and G. Wang
    IEEE Transactions on Medical Imaging, 40(8), 2170-2181, 2021
    [doi][arXiv]

  4. When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework
    Z. Huang, J. Zhang and H. Shan
    In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Online, Jun. 19-25, 2021
    [arXiv][code](Oral, 4.19% acceptance rate)

  5. Convolutional Ordinal Regression Forest for Image Ordinal Estimation
    H. Zhu*, H. Shan*, Y. Zhang, L. Che, X. Xu, J. Zhang, J. Shi and F. Y. Wang
    IEEE Transactions on Neural Networks and Learning Systems, 33(7), 2021
    [doi][arXiv] (* equal contribution)

  6. Meta Ordinal Weighting Net for Improving Lung Nodule Classification
    Y. Lei, H. Shan and J. Zhang
    In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, Ontario, Canada, Jun. 6-11, 2021
    [doi][arXiv]

  7. RoutingGAN: Routing Age Progression and Regression with Disentangled Learning
    Z. Huang, J. Zhang and H. Shan
    In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, Ontario, Canada, Jun. 6-11, 2021
    [doi][arXiv]

  8. SelfGait: A Spatiotemporal Representation Learning Method for Self-Supervised Gait Recognition
    Y. Liu, Y. Zeng, J. Pu, H. Shan, P. He and J. Zhang
    In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, Ontario, Canada, Jun. 6-11, 2021
    [doi][arXiv]

  9. Optimized Collusion Prevention for Online Exams during Social Distancing
    M. Li, L. Luo, S. Sikdar, N. I. Nizam, S. Gao, H. Shan, M. Kruger, U. Kruger, H. Mohamed, L. Xia and G. Wang
    npj Science of Learning, 6, 5, 2021
    [doi][code][Press: Phys.org, SCIENMAG, EurekAlert!, and so on]

  10. Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low Dose PET Image Denoising
    Y. Gong, H. Shan, Y. Teng, N. Tu, M. Li, G. Liang, G. Wang and S. Wang
    IEEE Transactions on Radiation and Plasma Medical Sciences, 5(2), 213-223, 2021
    [doi][arXiv][code]

  11. PFA-GAN: Progressive Face Aging with Generative Adversarial Network
    Z. Huang, S. Chen, J. Zhang and H. Shan
    IEEE Transactions on Information Forensics and Security, 16, 2031-2045, 2021
    [doi][arXiv][sup][code]


2020

  1. Meta Ordinal Regression Forests for Learning with Indeterminate Lung Nodules
    Y. Lei, H. Zhu, J. Zhang and H. Shan
    In Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Online, Dec. 16-19, 2020
    [doi][arXiv]

  2. Deep Efficient End-to-end Reconstruction (DEER) Network for Few-view Breast CT Image Reconstruction
    H. Xie, H. Shan, W. Cong, X. Zhang, S. Liu, R. Ning and G. Wang
    IEEE Access, 8, 196633-196646, 2020
    [doi][arXiv][code]

  3. Multi-Contrast Super-Resolution MRI Through a Progressive Network
    Q. Lyu, H. Shan, C. Steber, C. Helis, C. Whitlow, M. Chan and G. Wang
    IEEE Transactions on Medical Imaging, 39(9), 2738-2749, 2020
    [doi][arXiv]

  4. A Method of Rapid Quantification of Patient‐Specific Organ Doses for CT Using Deep‐Learning based Multi‐Organ Segmentation and GPU‐accelerated Monte Carlo Dose Computing
    Z. Peng, X. Fang, P. Yan, H. Shan, T. Liu, X. Pei, G. Wang, B. Liu, M. K. Kalra and X. G. Xu
    Medical Physics, 47(6), 2526-2536, 2020
    [doi][arXiv]

  5. Synergizing Medical Imaging and Radiotherapy with Deep Learning
    H. Shan*, X. Jia*, P. Yan, Y. Li, H. Paganetti and G. Wang
    Machine Learning: Science and Technology, 1(2), 021001, 2020
    [doi] (* equal contribution)

  6. Quadratic Autoencoder (Q-AE) for Low-Dose CT Denoising
    F. Fan, H. Shan, M. K. Kalra, R. Singh, G. Qian, M. Getzin, Y. Teng, J. Hahn and G. Wang
    IEEE Transactions on Medical Imaging, 39(6), 2035-2050, 2020
    [doi][arXiv][code]

  7. Deep Adversarial Network for Super Stimulated Emission Depletion Imaging
    M. Li, H. Shan, S. Pryshchep, M. M. Lopez and G. Wang
    Journal of Nanophotonics, 14(1), 016009, 2020
    [doi]

  8. MRI Super-Resolution with Ensemble Learning and Complementary Priors
    Q. Lyu, H. Shan and G. Wang
    IEEE Transactions on Computational Imaging, 6, 615-624, 2020
    [doi][arXiv]

  9. Look Globally, Age Locally: Face Aging with an Attention Mechanism
    H. Zhu, Z. Huang, H. Shan and J. Zhang
    In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, May 4-8, 2020
    [doi][arXiv][code](Oral)

  10. 3D Few-view CT Image Reconstruction with Deep Learning
    H. Xie, H. Shan and G. Wang
    In Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI) Workshop, Iowa City, Iowa, USA, Apr. 3-7, 2020
    [doi]

  11. Deeply-Supervised Multi-Dosage Prior Learning for Low-Dose PET Imaging
    Y. Gong, H. Shan, Y. Teng, H. Zheng, G. Wang and S. Wang
    In Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI) Workshop, Iowa City, Iowa, USA, Apr. 3-7, 2020
    [doi]

  12. Low-Dose PET Image Restoration with 2D and 3D Network Prior Learning
    Y. Gong, H. Shan, Y. Teng, H. Zheng, G. Wang and S. Wang
    In Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI) Workshop, Iowa City, Iowa, USA, Apr. 3-7, 2020
    [doi]

  13. Ordinal Distribution Regression for Gait-based Age Estimation
    H. Zhu, Y. Zhang, G. Li, J. Zhang and H. Shan
    SCIENCE CHINA Information Sciences, 63(2), 120102, 2020
    [doi][arXiv]

  14. Shape and Margin-Aware Lung Nodule Classification in Low-Dose CT Images via Soft Activation Mapping
    Y. Lei, Y. Tian, H. Shan, J. Zhang, G. Wang and M. K. Kalra
    Medical Image Analysis, 60, 101628, 2020
    [doi][arXiv]

  15. CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE)
    C. You, G. Li, Y. Zhang, X. Zhang, H. Shan, S. Ju, Z. Zhao, Z. Zhang, W. Cong, M. Vannier, P. Saha, E. A. Hoffman and G. Wang
    IEEE Transactions on Medical Imaging, 39(1), 188-203, 2020
    [doi][arXiv][code] (ESI highly cited paper)


2019

  1. Competitive Performance of a Modularized Deep Neural Network Compared to Commercial Algorithms for Low-Dose CT Image Reconstruction
    H. Shan, A. Padole, F. Homayounieh, U. Kruger, R. D. Khera, C. Nitiwarangkul, M. K. Kalra and G. Wang
    Nature Machine Intelligence, 1(6), 269–276, 2019
    [doi][arXiv][pdf][sup][code][Press: NIH/NIBIB, RPI News, EurekAlert!, Physicsworld, HealthImaging, Value Walk, and so on] (ESI highly cited paper, World Artificial Intelligence Conference Youth Outstanding Paper Award)

  2. Deep Encoder-Decoder Adversarial Reconstruction (DEAR) Network for 3D CT from Few-View Data
    H. Xie, H. Shan and G. Wang
    Bioengineering, 6(4), 111, 2019
    [doi][arXiv]

  3. A Dual-Stream Deep Convolutional Network for Reducing Metal Streak Artifacts in CT Images
    L. Gjesteby, H. Shan, Q. Yang, Y. Xi, Y. Jin, D. Giantsoudi, H. Paganetti, B. De Man and G. Wang
    Physics in Medicine and Biology, 64(23), 235003, 2019
    [doi]

  4. Accelerated Correction of Reflection Artifacts by Deep Neural Networks in Photo-Acoustic Tomography
    H. Shan, G. Wang and Y. Yang
    Applied Sciences, 9, 2615, 2019
    [doi]

  5. Low-Dose CT Simulation with a Generative Adversarial Network
    H. Shan, X. Jia, K. Mueller, U. Kruger and G. Wang
    In Proceedings of SPIE 11113, Developments in X-Ray Tomography XII, 111131F, San Diego, California, United States, Aug. 11-15, 2019
    [doi](Oral)

  6. Simultaneous Reconstruction of the Initial Pressure and Sound Speed in Photoacoustic Tomography Using a Deep-Learning Approach
    H. Shan, C. Wiedeman, G. Wang and Y. Yang
    In Proceedings of SPIE 11105, Novel Optical Systems, Methods, and Applications XXII, 1110504, San Diego, California, United States, Aug. 11-15, 2019
    [doi][arXiv](Oral)

  7. Deep-Learning-based Breast CT for Radiation Dose Reduction
    W. Cong, H. Shan, X. Zhang, S. Liu, R. Ning and G. Wang
    In Proceedings of SPIE 11113, Developments in X-Ray Tomography XII, 111131L, San Diego, California, United States, Aug. 11-15, 2019
    [doi][arXiv]

  8. Dual Network Architecture for Few-View CT - Trained on ImageNet Data and Transferred for Medical Imaging
    H. Xie, H. Shan, W. Cong, X. Zhang, S. Liu, R. Ning and G. Wang
    In Proceedings of SPIE 11113, Developments in X-Ray Tomography XII, 111130V, San Diego, California, United States, Aug. 11-15, 2019
    [doi][arXiv]

  9. Quadratic Neural Networks for CT Metal Artifact Reduction
    F. Fan, H. Shan, L. Gjesteby and G. Wang
    In Proceedings of SPIE 11113, Developments in X-Ray Tomography XII, 111130W, San Diego, California, United States, Aug. 11-15, 2019
    [doi]

  10. Super-Resolution MRI and CT Through GAN-CIRCLE
    Q. Lyu, C. You, H. Shan, Y. Zhang and G. Wang
    In Proceedings of SPIE 11113, Developments in X-Ray Tomography XII, 111130X, San Diego, California, United States, Aug. 11-15, 2019
    [doi][arXiv]

  11. Deep Learning Based CT Thermometry for Thermal Tumor Ablation
    N. Wang, M. Li, H. Shan and P. Yan
    In Proceedings of SPIE 11113, Developments in X-Ray Tomography XII, 111131T, San Diego, California, United States, Aug. 11-15, 2019
    [doi]

  12. Framework of Randomized Distribution Features for Visual Representation and Categorization
    H. Shan, J. Zhang and U. Kruger
    IEEE Transactions on Cybernetics, 49(9), 3599-3606, 2019
    [doi]

  13. MCDNet – A Denoising Convolutional Neural Network to Accelerate Monte Carlo Radiation Transport Simulations: A Proof of Principle with Patient Dose from X-Ray CT Imaging
    Z. Peng*, H. Shan*, T. Liu, X. Pei, G. Wang and X. G. Xu
    IEEE Access, 7, 76680-76689, 2019
    [doi] (* equal contribution)

  14. A Two-dimensional Feasibility Study of Deep Learning-based Feature Detection and Characterization Directly from CT Sinograms
    Q. De Man, E. Haneda, B. Claus, P. Fitzgerald, B. De Man, G. Qian, H. Shan, J. Min, M. Sabuncu and G. Wang
    Medical Physics, 46(12), e790-e800, 2019
    [doi]

  15. A Novel Transfer Learning Framework for Low-Dose CT
    H. Shan, U. Kruger and G. Wang
    In Proceedings of SPIE 11072, the 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 110722Y, Philadelphia, PA, USA, June 2-6, 2019
    [doi]

  16. Quadratic Autoencoder for Low-Dose CT Denoising
    F. Fan, H. Shan and G. Wang
    In Proceedings of SPIE 11072, the 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 110722Z, Philadelphia, PA, USA, June 2-6, 2019
    [doi][arXiv]

  17. Crowd Counting with Limited Labeling through Submodular Frame Selection
    Q. Zhou, J. Zhang, L. Che, H. Shan and J. Wang
    IEEE Transactions on Intelligent Transportation Systems, 20(5), 1728-1738, 2019
    [doi]

  18. Multi-Task GANs for View-Specific Feature Learning in Gait Recognition
    Y. He, J. Zhang, H. Shan and L. Wang
    IEEE Transactions on Information Forensics and Security, 14(1), 102-113, 2019
    [doi][code]


2018

  1. 3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network
    H. Shan, Y. Zhang, Q. Yang, U. Kruger, M. K. Kalra, L. Sun, W. Cong and G. Wang
    IEEE Transactions on Medical Imaging, 37(6), 1522-1534, 2018
    [doi][arXiv][code][Press: Lindau Nobel Laureate Meetings] (ESI highly cited paper)

  2. Structurally-Sensitive Multi-Scale Deep Neural Network for Low-Dose CT Denoising
    C. You, Q. Yang, H. Shan, L. Gjesteby, L. Guang, S. Ju, Z. Zhang, Z. Zhao, Y. Zhang, W. Cong and G. Wang
    IEEE Access, 6, 41839-41855, 2018
    [doi][arXiv]

  3. Deep Neural Network for CT Metal Artifact Reduction with a Perceptual Loss Function
    L. Gjesteby, H. Shan, Q. Yang, Y. Xi, B. Claus, Y. Jin, B. De Man and G. Wang
    In Proceedings of The Fifth International Conference on Image Formation in X-ray Computed Tomography (CTMeeting), Salt Lake City, Utah, USA, May 20-23, 2018
    [pdf]

  4. Real-Valued Multivariate Dimension Reduction: A Survey
    H. Shan and J. Zhang
    Acta Automatica Sinica, 44(2), 192-215, 2018
    [doi][pdf] (in Chinese)

  5. A Maximum Contributed Component Regression for the Inverse Problem in Optical Scatterometry
    H. Zhu, Y. Lee, H. Shan and J. Zhang
    Optics Express, 25(14), 15956-15966, 2017
    [doi][pdf]

  6. Population Density-based Hospital Recommendation with Mobile LBS Big Data
    H. Chao, Y. Cao, J. Zhang, F. Xia, Y. Zhou and H. Shan
    In Proceedings of IEEE International Conference on Big Data and Smart Computing (BigComp), Shanghai, China, Jan. 15-18, 2018
    [doi]


2017

  1. Deep Learning Methods for CT Image-Domain Metal Artifact Reduction
    L. Gjesteby, Q. Yang, Y. Xi, H. Shan, B. Claus, Y. Jin, B. De Man and G. Wang
    In Proceedings of SPIE 10391, Developments in X-Ray Tomography XI, 103910W, San Diego, California, United States, Aug. 6-10, 2017
    [doi][pdf](Oral)

  2. Enhancing Transferability of Features from Pretrained Deep Neural Networks for Lung Nodule Classification
    H. Shan, G. Wang, M. K. Kalra, R. C. de Souza and J. Zhang
    In Proceedings of the 14th International Conference on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), Xi'an, China, June 18-23, 2017
    [doi][pdf](Oral)


2016

  1. Learning Linear Representation of Space Partitioning Trees based on Unsupervised Kernel Dimension Reduction
    H. Shan, J. Zhang and U. Kruger
    IEEE Transactions on Cybernetics, 46(12), 3427-3438, 2016
    [doi][sup]

  2. Group Information-based Dimensionality Reduction via Canonical Correlation Analysis
    H. Zhu, H. Shan, Y. Lee, Y. He, Q. Zhou and J. Zhang
    In Proceedings of the 23rd International Conference on Neural Information Processing (ICONIP), Kyoto, Japan, Oct. 16-21, 2016
    [doi][pdf]

  3. Randomized Distribution Feature for Image Classification
    H. Shan and J. Zhang
    In Proceedings of the 22nd European Conference on Artificial Intelligence (ECAI), The Hague, Holland, Aug. 29-Sep. 2, 2016
    [doi][pdf][sup](Oral)


2013

  1. Utilizing Density Estimation to Count Objects
    W. Xia and H. Shan
    Journal of Frontiers of Computer Science and Technology, 7(11), 1002-1008, 2013
    [pdf] (in Chinese)