Selected Highlights

  • 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 and A New Benchmark
    Z. Huang, J. Zhang and H. Shan
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6), 7917-7932, 2023
    [doi][arXiv][code] (Journal version of CVPR 2021 Oral paper)

  • Learning Representation for Clustering via Prototype Scattering and Positive Sampling
    Z. Huang, J. Chen, J. Zhang and H. Shan
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6), 7509-7524, 2023
    [doi][arXiv][code]

  • 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, Photonics Media] (ESI highly cited paper)

  • LICO: Explainable Models with Language-Image Consistency
    Y. Lei, Z. Li, Y. Li, J. Zhang and H. Shan
    In Proceedings of International Conference on Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, Dec. 10-16, 2023
    [doi][arXiv][code]

  • 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]


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


Papers

2024

  1. CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization
    Q. Gao, Z. Li, J. Zhang, Y. Zhang and H. Shan
    IEEE Transactions on Medical Imaging, 43(2), 745-759, 2024
    [doi][arXiv][code]

  2. Quad-Net: Quad-domain Network for CT Metal Artifact Reduction
    Z. Li, Q. Gao, Y. Wu, C. Niu, J. Zhang, M. Wang, G. Wang and H. Shan
    IEEE Transactions on Medical Imaging, 43(5), 2024
    [doi][arXiv][code]

  3. LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and Deblurring
    Z. Chen, C. Niu, Q. Gao, G. Wang* and H. Shan*
    IEEE Transactions on Medical Imaging, 43(5), 2024
    [doi][arXiv][code]

  4. Joint Learning Framework of Cross-modal Synthesis and Diagnosis for Alzheimer's Disease by Mining Underlying Shared Modality Information
    C. Wang, S. Piao, Z. Huang, Q. Gao, J. Zhang, Y. Li* and H. Shan*
    Medical Image Analysis, 91(2024), 103032, 2024
    [doi][sup][code]

  5. DreamVideo: Composing Your Dream Videos with Customized Subject and Motion
    Y. Wei, S. Zhang, Z. Qing, H. Yuan, Z. Liu, Y. Liu, Y. Zhang, J. Zhou and H. Shan
    In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle WA, USA, Jun. 17-21, 2024
    [arXiv][code]

  6. Point, Segment and Count: A Generalized Framework for Object Counting
    Z. Huang, M. Dai, Y. Zhang, J. Zhang and H. Shan
    In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle WA, USA, Jun. 17-21, 2024
    [arXiv][code]

  7. FLDM-VTON: Faithful Latent Diffusion Model for Virtual Try-on
    C. Wang, T. Chen, Z. Chen, Z. Huang, T. Jiang, Q. Wang and H. Shan
    In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Jeju Island, South Korea, Aug. 3-9, 2024

  8. HOPE: Hybrid-granularity Ordinal Prototype Learning for Progression Prediction of Mild Cognitive Impairment
    C. Wang, Y. Lei, T. Chen, J. Zhang, Y. Li* and H. Shan*
    IEEE Journal of Biomedical and Health Informatics, 2024 (In Press)
    [doi][arXiv][code]

  9. CT Image Denoising and Deblurring with Deep Learning: Current Status and Perspectives
    Y. Lei, C. Niu*, J. Zhang, G. Wang and H. Shan*
    IEEE Transactions on Radiation and Plasma Medical Sciences, 8(2), 153-172, 2024
    [doi]

  10. Prompt Learning in Computer Vision: A Survey
    Y. Lei*, J. Li, Z. Li, Y. Cao and H. Shan*
    Frontiers of Information Technology & Electronic Engineering, 25(1), 42-63, 2024
    [doi]

  11. Promoting Fast MR Imaging Pipeline by Full-Stack AI
    Z. Wang, B. Li, H. Yu, Z. Zhang, M. Ran, W. Xia, Z. Yang, J. Lu, H. Chen, J. Zhou, H. Shan* and Y. Zhang*
    iScience, 27(1), 108608, 2024
    [doi][sup][code]

  12. End-to-end Paired Ambisonic-Binaural Audio Rendering
    Y. Zhu, Q. Kong, J. Shi, S. Liu, X. Ye, J.C. Wang, H. Shan and J. Zhang
    IEEE/CAA Journal of Automatica Sinica, 11(2), 502-513, 2024
    [doi]

  13. Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting
    J. Gao, Z. Huang, Y. Lei, H. Shan, J. Z. Wang, F. Y. Wang and J. Zhang
    IEEE Transactions on Neural Networks and Learning Systems, 2023 (In Press)
    [doi][arXiv][code]

  14. Weakly Supervised Learning-based 3D Bladder Reconstruction from 2D Ultrasound Images for Bladder Volume Measurement
    Z. Peng, H. Shan, X. Yang, S. Li, D. Tang, Y. Cao, Q. Shao, W. Huo and Z. Yang
    Medical Physics, 51(2), 1277-1288, 2024
    [doi]

  15. SIAM: A Simple Alternating Mixer for Video Prediction
    X. Zheng, Z. Peng, Y. Cao, H. Shan and J. Zhang
    In Proceedings of IEEE International Conference on Multimedia and Expo (ICME), Niagara Falls, ON, Canada, Jul. 15-19, 2024
    [arXiv]

  16. Semantic Latent Decomposition with Normalizing Flows for Face Editing
    B. Li, Z. Huang, H. Shan and J. Zhang
    In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Korea, Apr. 14-19, 2024
    [doi][arXiv][code](Oral)

  17. Potential and Prospects of Segment Anything Model: A Survey
    M. Wang, Z. Huang, H. He, H. Lu, H. Shan and J. Zhang
    Journal of Image and Graphics (中国图象图形学报), 2024
    [doi] (in Chinese)


2023

  1. Learning Representation for Clustering via Prototype Scattering and Positive Sampling
    Z. Huang, J. Chen, J. Zhang and H. Shan
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6), 7509-7524, 2023
    [doi][arXiv][code] (ESI highly cited paper)

  2. When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework and A New Benchmark
    Z. Huang, J. Zhang and H. Shan
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6), 7917-7932, 2023
    [doi][arXiv][code]

  3. LICO: Explainable Models with Language-Image Consistency
    Y. Lei, Z. Li, Y. Li, J. Zhang and H. Shan
    In Proceedings of International Conference on Neural Information Processing Systems (NeurIPS), New Orleans, Louisiana, USA, Dec. 10-16, 2023
    [doi][arXiv][code]

  4. Learning to Distill Global Representation for Sparse-View CT
    Z. Li, C. Ma, J. Chen, J. Zhang and H. Shan
    In Proceedings of International Conference on Computer Vision (ICCV), Paris, France, Oct. 2-6, 2023
    [doi][arXiv][sup][code]

  5. Adaptive Nonlinear Latent Transformation for Conditional Face Editing
    Z. Huang, S. Ma, J. Zhang and H. Shan
    In Proceedings of International Conference on Computer Vision (ICCV), Paris, France, Oct. 2-6, 2023
    [doi][arXiv][sup][code]

  6. Online Prototype Learning for Online Continual Learning
    Y. Wei, J. Ye, Z. Huang, J. Zhang and H. Shan
    In Proceedings of International Conference on Computer Vision (ICCV), Paris, France, Oct. 2-6, 2023
    [doi][arXiv][sup][code]

  7. Twin Contrastive Learning with Noisy Labels
    Z. Huang, J. Zhang and H. Shan
    In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, Jun. 18-22, 2023
    [doi][arXiv][sup][code]

  8. Emo-DNA: Emotion Decoupling and Alignment Learning for Cross-Corpus Speech Emotion Recognition
    J. Ye, Y. Wei, X. C. Wen, C. Ma, Z. Huang, K. H. Liu and H. Shan
    In Proceedings of ACM International Conference on Multimedia (ACM MM), Ottawa, Ontario, Canada, Oct. 29-Nov. 2, 2023
    [doi][arXiv][code]

  9. Impact of Loss Functions on the Performance of a Deep Neural Network Designed to Restore Low-dose Digital Mammography
    H. Shan†, R. B. Vimieiro†, L. R. Borges, M. A. C. Vieira and G. Wang
    Artificial Intelligence In Medicine, 142(2023), 102555, 2023
    [doi][arXiv][code]

  10. M3NAS: Multi-Scale and Multi-Level Memory-Efficient Neural Architecture Search for Low-Dose CT Denoising
    Z. Lu, W. Xia, Y. Huang, M. Hou, H. Chen, J. Zhou, H. Shan* and Y. Zhang*
    IEEE Transactions on Medical Imaging, 42(3), 850-863, 2023
    [doi][arXiv]

  11. FreeSeed: Frequency-band-aware and Self-guided Network for Sparse-view CT Reconstruction
    C. Ma, Z. Li, J. Zhang, Y. Zhang and H. Shan
    In Proceedings of International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Vancouver, Canada, Oct. 8-12, 2023
    [doi][arXiv][code]

  12. ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising
    Z. Chen, Q. Gao, Y. Zhang and H. Shan
    In Proceedings of International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Vancouver, Canada, Oct. 8-12, 2023
    [doi][arXiv][code]

  13. CLIP-Lung: Textual Knowledge-Guided Lung Nodule Malignancy Prediction
    Y. Lei, Z. Li, Y. Shen, J. Zhang and H. Shan
    In Proceedings of International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Vancouver, Canada, Oct. 8-12, 2023
    [doi][arXiv][code]

  14. BerDiff: Conditional Bernoulli Diffusion Model for Medical Image Segmentation
    T. Chen, C. Wang and H. Shan
    In Proceedings of International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Vancouver, Canada, Oct. 8-12, 2023
    [doi][arXiv][code]

  15. Geometry Flow-based Deep Riemannian Metric Learning
    Y. Li, C. Fei, C. Wang, H. Shan and R. Lu
    IEEE/CAA Journal of Automatica Sinica, 10(9), 1882-1892, 2023
    [doi]

  16. Mutual Information Boosted Reweighting for Precipitation Nowcasting from Radar Images
    Y. Cao, D. Zhang, X. Zheng, H. Shan and J. Zhang
    Remote Sensing, 15(6), 1639, 2023
    [doi]

  17. Motion Matters: A Novel Motion Modeling For Cross-View Gait Feature Learning
    J. Li, J. Gao, Y. Zhang, H. Shan and J. Zhang
    In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, Jun. 4-10, 2023
    [doi][arXiv]

  18. GaitCoTr: Improved Spatial-Temporal Representation for Gait Recognition with a Hybrid Convolution-Transformer Framework
    J. Li, Y. Zhang, H. Shan and J. Zhang
    In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, Jun. 4-10, 2023
    [doi][code]

  19. Mutual Information based Reweighting for Precipitation Nowcasting
    Y. Cao, D. Zhang, X. Zheng, H. Shan and J. Zhang
    In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, Jun. 4-10, 2023
    [doi]

  20. Cross-Head Supervision for Crowd Counting with Noisy Annotations
    M. Dai, Z. Huang, J. Gao, H. Shan and J. Zhang
    In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, Jun. 4-10, 2023
    [doi][arXiv][code]

  21. FAN-Net: Fourier-based Adaptive Normalization for Cross-Domain Stroke Lesion Segmentation
    W. Yu, Y. Lei and H. Shan
    In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, Jun. 4-10, 2023
    [doi][arXiv]

  22. Temporal Modeling Matters: A Novel Temporal Emotional Modeling Approach for Speech Emotion Recognition
    J. Ye, X. C. Wen, Y. Wei, Y. Xu, K. H. Liu* and H. Shan*
    In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, Jun. 4-10, 2023
    [doi][code]

  23. DO-FAM: Disentangled Non-Linear Latent Navigation for Facial Attribute Manipulation
    Y. Yuan, S. Ma, H. Shan and J. Zhang
    In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, Jun. 4-10, 2023
    [doi][code](Oral)

  24. GREAT-IQA: Integrating Global Perception and Local Task-Specific Information for CT Image Quality Assessment
    Q. Gao, H. Shan and D. Zeng
    In Proceedings of IEEE International Conference on Medical Artificial Intelligence (MedAI), Beijing, China, Nov. 18-19, 2023
    [doi](Oral)

  25. DSiV: Data Science for Intelligent Vehicles
    J. Zhang, J. Pu, J. Chen, H. Fu, Y. Tao, S. Wang, Q. Chen, Y. Xiao, S. Chen, Y. Cheng, H. Shan, D. Chen and F. Y. Wang
    IEEE Transactions on Intelligent Vehicles, 8(4), 2628-2634, 2023
    [doi]

  26. Material Decomposition of Spectral CT Images via Attention-based Global Convolutional Generative Adversarial Network
    X. Guo, P. He*, X. Lv, X. Ren, Y. Li, Y. Liu, X. Lei, P. Feng and H. Shan*
    Nuclear Science and Techniques, 34(3), 45, 2023
    [doi]

  27. SAN-Net: Learning Generalization to Unseen Sites for Stroke Lesion Segmentation with Self-Adaptive Normalization
    W. Yu, Z. Huang, J. Zhang and H. Shan
    Computers in Biology and Medicine, 156, 106717, 2023
    [doi][arXiv][code]

  28. Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A Survey
    W. Xia, H. Shan, G. Wang and Y. Zhang
    IEEE Signal Processing Magazine, 40(2), 89-100, 2023
    [doi][arXiv]

  29. Forget Less, Count Better: A Domain-Incremental Self-Distillation Learning Benchmark for Lifelong Crowd Counting
    J. Gao, J. Li, H. Shan, Y. Qu, J. Z. Wang, F. Y. Wang and J. Zhang
    Frontiers of Information Technology & Electronic Engineering, 24(2), 187-202, 2023
    [doi][arXiv] (Cover article)

  30. A Survey of Deep Learning-Based MRI Stroke Lesion Segmentation Methods
    W. Yu, T. Chen, J. Zhang and H. Shan
    Chinese Journal of Intelligent Science and Technology (智能科学与技术学报), 5(3), 293-312, 2023
    [doi] (in Chinese)


2022

  1. SPICE: Semantic Pseudo-Labeling for Image Clustering
    C. Niu, H. Shan* and G. Wang*
    IEEE Transactions on Image Processing, 31, 7264-7278, 2022
    [doi][arXiv][code]

  2. Meta Ordinal Regression Forest for Medical Image Classification with Ordinal Labels
    Y. Lei, H. Zhu, J. Zhang and H. Shan
    IEEE/CAA Journal of Automatica Sinica, 9(7), 1233-1247, 2022
    [doi][arXiv]

  3. DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising
    Z. Huang, J. Zhang, Y. Zhang and H. Shan
    IEEE Transactions on Instrumentation and Measurement, 71, 4500512, 2022
    [doi][arXiv][code] (ESI highly cited paper)

  4. 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), 4084-4095, 2022
    [doi][arXiv]

  5. CoCoDiff: A Contextual Conditional Diffusion Model for Low-dose CT Image Denoising
    Q. Gao and H. Shan
    In Proceedings of SPIE 12242, Developments in X-Ray Tomography XIV, San Diego, California, United States, Aug. 22-24, 2022
    [doi]

  6. OpenKBP-Opt: An International and Reproducible Evaluation of 76 Knowledge-Based Planning Pipelines
    A. Babier, et al.
    Physics in Medicine and Biology, 67(18), 185012, 2022
    [doi][arXiv][code]

  7. 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
    IEEE Transactions on Radiation and Plasma Medical Sciences, 6(6), 656-666, 2022
    [doi][arXiv]

  8. Stabilizing Deep Tomographic Reconstruction: Part A. Hybrid Framework and Experimental Results
    W. Wu, D. Hu, W. Cong, H. Shan, S. Wang, C. Niu, P. Yan, H. Yu, V. Vardhanabhuti and G. Wang
    Patterns, 3, 100474, 2022
    [doi][arXiv][code]

  9. Stabilizing Deep Tomographic Reconstruction: Part B. Convergence Analysis and Adversarial Attacks
    W. Wu, D. Hu, W. Cong, H. Shan, S. Wang, C. Niu, P. Yan, H. Yu, V. Vardhanabhuti and G. Wang
    Patterns, 3, 100475, 2022
    [doi][arXiv][code]

  10. Hybrid Weighting Loss for Precipitation Nowcasting from Radar Images
    Y. Cao, L. Chen, D. Zhang, L. Ma and H. Shan
    Proceedings of IEEE International Conference on Acoustics, Speech, & Signal Processing (ICASSP), Singapore, May 22-27, 2022
    [doi]

  11. Low-Dose CT Denoising via Neural Architecture Search
    Z. Lu, W. Xia, Y. Huang, M. Hou, H. Chen, H. Shan* and Y. Zhang*
    Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI), ITC Royal Bengal, Kolkata, India, Mar. 28-31, 2022.
    [doi]

  12. Content-Noise Complementary Learning for Medical Image Denoising
    M. Geng, X. Meng, J. Yu, L. Zhu, L. Jin, Z. Jiang, B. Qiu, H. Li, H. Kong, J. Yuan, K. Yang, H. Shan, H. Han, Z. Yang, Q. Ren and Y. Lu
    IEEE Transactions on Medical Imaging, 41(2), 407-419, 2022
    [doi][code] (ESI highly cited paper)

  13. A Survey of Problem Setting-driven Deep Reinforcement Learning
    Z. Zhang, B. Zhao, H. Shan and J. Zhang
    Pattern Recognition and Artificial Intelligence (模式识别与人工智能), 35(8), 718-742, 2022
    [doi][pdf] (in Chinese)


2021

  1. 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
    [doi][arXiv][sup][code](Oral, 4.19% acceptance rate)

  2. 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
    [doi][arXiv][code](13.9% acceptance rate)

  3. 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]

  4. 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]

  5. Strided Self-Supervised Low-Dose CT Denoising for Lung Nodule Classification
    Y. Lei, J. Zhang and H. Shan
    Phenomics, 1(6), 257-268, 2021
    [doi][code]

  6. An Ensemble Learning Method Based on Ordinal Regression for COVID-19 Diagnosis from Chest CT
    X. Guo, Y. Lei, P. He*, W. Zeng, R. Yang, Y. Ma, P. Feng, Q. Lyu, G. Wang and H. Shan*
    Physics in Medicine and Biology, 66, 244001, 2021
    [doi]

  7. Application of Deep-Learning Based Monte Carlo Denoising for Fast Radiation Treatment Dose Calculations
    Z. Peng, H. Shan, J. Zhou, X. Pei, A. Wu and X. G. Xu
    Proceedings of IEEE International Conference on Medical Imaging Physics and Engineering (ICMIPE), Hefei, China, Nov. 12-14, 2021
    [doi][arXiv]

  8. Feasibility Evaluation of PET Scan-Time Reduction for Diagnosing Amyloid-β Levels in Alzheimer’s Disease Patients Using a Deep-Learning-based Denoising Algorithm
    Z. Peng, M. Ni, H. Shan, Y. Lu, Y. Li, Y. Zhang, X. Pei, Z. Chen, S. Wang and X. G. Xu
    Computers in Biology and Medicine, 138, 104919, 2021
    [doi]

  9. 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]

  10. 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]

  11. 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]

  12. 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]

  13. 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]

  14. 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]


2020

  1. 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]

  2. 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]

  3. 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]

  4. 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]

  5. 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]

  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(2020), 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. 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. 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]

  4. 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)

  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. 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]

  7. 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]

  8. 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]

  9. 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]

  10. 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]

  11. 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]

  12. 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]

  13. 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]

  14. 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]

  15. 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]

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

  3. 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]

  4. 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]

  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. 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)

  2. 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)


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. 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)

  3. 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]


2013

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