My work explores image generation, federated learning, and AI systems for healthcare. I am interested in building learning methods that remain useful when data is distributed, privacy-sensitive, noisy, or limited.
Recent projects include federated visual primitive sharing, reliable multi-label medical image diagnosis, LLM-orchestrated CT reconstruction, and deep learning methods for medical imaging tasks such as OCT speckle reduction, CT reconstruction, low-dose CT denoising, and MRI reconstruction.
2026
LLM-Orchestrated Diagnose-Plan-Treat for Mixed-Degradation CT Reconstruction
Huang, Y., Chen, Y., Xu, F., Wang, T., Xia, W., Shan, H., Zhang, Y.
International Joint Conference on Artificial Intelligence (IJCAI) Accepted 2026
Accepted to IJCAI 2026. The work explores an LLM-orchestrated diagnose-plan-treat framework for mixed-degradation CT reconstruction.
# medical imaging # CT # reconstruction # large models
LLM-Orchestrated Diagnose-Plan-Treat for Mixed-Degradation CT Reconstruction
Huang, Y., Chen, Y., Xu, F., Wang, T., Xia, W., Shan, H., Zhang, Y.
International Joint Conference on Artificial Intelligence (IJCAI) Accepted 2026
Accepted to IJCAI 2026. The work explores an LLM-orchestrated diagnose-plan-treat framework for mixed-degradation CT reconstruction.
# medical imaging # CT # reconstruction # large models
FACT: Fuzzy Alignment with Comorbidity Topology for Reliable Multi-Label Medical Image Diagnosis
Chen, Y., Huang, Y., Qin, Y., Yang, Z., Yuan, L., Ran, M., Zhang, Y.
International Conference on Machine Learning (ICML) Regular Paper 2026
Accepted as an ICML 2026 regular paper. The work studies reliable multi-label medical image diagnosis through fuzzy alignment with comorbidity topology.
# medical imaging # AI in healthcare # trustworthy AI # multi-label diagnosis
FACT: Fuzzy Alignment with Comorbidity Topology for Reliable Multi-Label Medical Image Diagnosis
Chen, Y., Huang, Y., Qin, Y., Yang, Z., Yuan, L., Ran, M., Zhang, Y.
International Conference on Machine Learning (ICML) Regular Paper 2026
Accepted as an ICML 2026 regular paper. The work studies reliable multi-label medical image diagnosis through fuzzy alignment with comorbidity topology.
# medical imaging # AI in healthcare # trustworthy AI # multi-label diagnosis
Beyond Class Boundaries: Federated Visual Primitive Sharing with Text-Guided Adaptation
Huang, Y., Chen, Y., Wang, T., Lu, Z., Shao, Z., Li, B., Zhang, Y.
Proceedings of the ACM Web Conference 2026 pp. 5275-5285 (CCF-A, Oral Presentation) 2026
Studies federated visual primitive sharing with text-guided adaptation, aiming to improve visual learning beyond fixed class boundaries in distributed settings.
# federated learning # image generation # vision-language
Beyond Class Boundaries: Federated Visual Primitive Sharing with Text-Guided Adaptation
Huang, Y., Chen, Y., Wang, T., Lu, Z., Shao, Z., Li, B., Zhang, Y.
Proceedings of the ACM Web Conference 2026 pp. 5275-5285 (CCF-A, Oral Presentation) 2026
Studies federated visual primitive sharing with text-guided adaptation, aiming to improve visual learning beyond fixed class boundaries in distributed settings.
# federated learning # image generation # vision-language
2025
Trustworthy Disentangled Framework for Multi-Label Medical Image Classification with Multimodal Refinement
Chen, Y., Yang, Z., Huang, Y., Yang, X., Yeo, S., Zhang, Y.
IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2025
Develops a trustworthy disentangled framework for multi-label medical image classification with multimodal refinement.
# medical imaging # AI in healthcare # multimodal
Trustworthy Disentangled Framework for Multi-Label Medical Image Classification with Multimodal Refinement
Chen, Y., Yang, Z., Huang, Y., Yang, X., Yeo, S., Zhang, Y.
IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2025
Develops a trustworthy disentangled framework for multi-label medical image classification with multimodal refinement.
# medical imaging # AI in healthcare # multimodal
SMART: Self-supervised Learning for Metal Artifact Reduction in Computed Tomography Using Range Null Space Decomposition
Wang, T., Cao, Y., Lu, Z., Huang, Y., Lu, J., Fan, F., Shan, H., Zhang, Y.
IEEE Transactions on Medical Imaging 2025
Proposes a self-supervised CT metal artifact reduction method based on range-null space decomposition and implicit neural representation.
# medical imaging # CT # self-supervised learning
SMART: Self-supervised Learning for Metal Artifact Reduction in Computed Tomography Using Range Null Space Decomposition
Wang, T., Cao, Y., Lu, Z., Huang, Y., Lu, J., Fan, F., Shan, H., Zhang, Y.
IEEE Transactions on Medical Imaging 2025
Proposes a self-supervised CT metal artifact reduction method based on range-null space decomposition and implicit neural representation.
# medical imaging # CT # self-supervised learning
Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval
Xie, Y., Yang, Z., Huang, Y., Chen, Y., Zhang, L., Liu, L., Zhang, Y.
arXiv preprint arXiv:2509.07532 2025
Introduces an uncertainty-driven hierarchical sampling and retrieval framework for continual malware detection under class imbalance and concept drift.
# continual learning # uncertainty sampling # malware detection
Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval
Xie, Y., Yang, Z., Huang, Y., Chen, Y., Zhang, L., Liu, L., Zhang, Y.
arXiv preprint arXiv:2509.07532 2025
Introduces an uncertainty-driven hierarchical sampling and retrieval framework for continual malware detection under class imbalance and concept drift.
# continual learning # uncertainty sampling # malware detection
Federated Learning for Large Models in Medical Imaging: A Comprehensive Review
Sun, M., Yang, Z., Huang, Y., Yu, H., Chen, Y., Qi, S., Teoh, A. B. J., Zhang, Y.
arXiv preprint arXiv:2508.20414 2025
Reviews federated learning for large models across the medical imaging pipeline, from reconstruction to clinical diagnosis and segmentation.
# federated learning # medical imaging # large models
Federated Learning for Large Models in Medical Imaging: A Comprehensive Review
Sun, M., Yang, Z., Huang, Y., Yu, H., Chen, Y., Qi, S., Teoh, A. B. J., Zhang, Y.
arXiv preprint arXiv:2508.20414 2025
Reviews federated learning for large models across the medical imaging pipeline, from reconstruction to clinical diagnosis and segmentation.
# federated learning # medical imaging # large models
FedRIR: Rethinking Information Representation in Federated Learning
Huang, Y., Shao, Z., Yang, Z., Lu, Z., Zhang, Y.
Proceedings of the ACM on Web Conference 2025 pp. 807-816 (CCF-A, Oral Presentation) 2025
Sydney, NSW, Australia.
Rethinks how information is represented and exchanged in federated learning, aiming to improve collaborative training under distributed data settings.
# federated learning # image generation
FedRIR: Rethinking Information Representation in Federated Learning
Huang, Y., Shao, Z., Yang, Z., Lu, Z., Zhang, Y.
Proceedings of the ACM on Web Conference 2025 pp. 807-816 (CCF-A, Oral Presentation) 2025
Sydney, NSW, Australia.
Rethinks how information is represented and exchanged in federated learning, aiming to improve collaborative training under distributed data settings.
# federated learning # image generation
2022
M3NAS: Multi-Scale and Multi-Level Memory-Efficient Neural Architecture Search for Low-Dose CT Denoising
Lu Z., Xia, W., Huang, Y., Hou, M., Chen, H., Zhou, J., Shan, H., Zhang, Y.
IEEE Transactions on Medical Imaging 42(3), pp. 850-863 2022
Proposes a multi-scale and multi-level neural architecture search strategy for memory-efficient low-dose CT denoising.
# medical imaging # CT # denoising
M3NAS: Multi-Scale and Multi-Level Memory-Efficient Neural Architecture Search for Low-Dose CT Denoising
Lu Z., Xia, W., Huang, Y., Hou, M., Chen, H., Zhou, J., Shan, H., Zhang, Y.
IEEE Transactions on Medical Imaging 42(3), pp. 850-863 2022
Proposes a multi-scale and multi-level neural architecture search strategy for memory-efficient low-dose CT denoising.
# medical imaging # CT # denoising
Multi-task short-term reactive and active load forecasting method based on attention-LSTM model
Qin, J., Zhang, Y., Fan, S., Hu, X., H., Huang, Y., Lu, Z., Liu, Y.
International Journal of Electrical Power & Energy Systems 135, 107517 2022
Develops an attention-LSTM based multi-task method for short-term reactive and active load forecasting.
# machine learning # time series
Multi-task short-term reactive and active load forecasting method based on attention-LSTM model
Qin, J., Zhang, Y., Fan, S., Hu, X., H., Huang, Y., Lu, Z., Liu, Y.
International Journal of Electrical Power & Energy Systems 135, 107517 2022
Develops an attention-LSTM based multi-task method for short-term reactive and active load forecasting.
# machine learning # time series
Low-dose CT denoising via neural architecture search
Lu, Z., Xia, W., Huang, Y., Hou, M., Chen, H., Shan, H., Zhang, Y.
IEEE International Symposium on Biomedical Imaging (ISBI) 2022
Kolkata, India.
Applies neural architecture search to low-dose CT denoising, seeking effective model structures for medical image restoration.
# medical imaging # CT # denoising
Low-dose CT denoising via neural architecture search
Lu, Z., Xia, W., Huang, Y., Hou, M., Chen, H., Shan, H., Zhang, Y.
IEEE International Symposium on Biomedical Imaging (ISBI) 2022
Kolkata, India.
Applies neural architecture search to low-dose CT denoising, seeking effective model structures for medical image restoration.
# medical imaging # CT # denoising
2021
MAGIC: Manifold and graph integrative convolutional network for low-dose CT reconstruction
Xia, W., Lu, Z., Huang, Y., Shi, Z., Liu, Y., Chen, H., Chen, Y., Zhou, J., Zhang, Y.
IEEE Transactions on Medical Imaging 40(12), pp. 3459-3472 2021
Proposes a manifold and graph integrative convolutional network for low-dose CT reconstruction.
# medical imaging # CT
MAGIC: Manifold and graph integrative convolutional network for low-dose CT reconstruction
Xia, W., Lu, Z., Huang, Y., Shi, Z., Liu, Y., Chen, H., Chen, Y., Zhou, J., Zhang, Y.
IEEE Transactions on Medical Imaging 40(12), pp. 3459-3472 2021
Proposes a manifold and graph integrative convolutional network for low-dose CT reconstruction.
# medical imaging # CT
CT reconstruction with PDF: Parameter-dependent framework for data from multiple geometries and dose levels
Xia, W., Lu, Z., Huang, Y., Liu, Y., Chen, H., Zhou, J., Zhang, Y.
IEEE Transactions on Medical Imaging 40(11), pp. 3065-3076 2021
Develops a parameter-dependent framework for CT reconstruction across multiple geometries and dose levels.
# medical imaging # CT
CT reconstruction with PDF: Parameter-dependent framework for data from multiple geometries and dose levels
Xia, W., Lu, Z., Huang, Y., Liu, Y., Chen, H., Zhou, J., Zhang, Y.
IEEE Transactions on Medical Imaging 40(11), pp. 3065-3076 2021
Develops a parameter-dependent framework for CT reconstruction across multiple geometries and dose levels.
# medical imaging # CT
One network to solve them all: A sequential multi-task joint learning network framework for MR imaging pipeline
Wang, Z., Xia, W., Lu, Z., Huang, Y., Liu, Y., Chen, H., Zhou, J., Zhang, Y.
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) (CCF-B) 2021
Strasbourg, France.
Presents a sequential multi-task joint learning network for multiple stages of the MR imaging pipeline.
# medical imaging # MRI
One network to solve them all: A sequential multi-task joint learning network framework for MR imaging pipeline
Wang, Z., Xia, W., Lu, Z., Huang, Y., Liu, Y., Chen, H., Zhou, J., Zhang, Y.
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) (CCF-B) 2021
Strasbourg, France.
Presents a sequential multi-task joint learning network for multiple stages of the MR imaging pipeline.
# medical imaging # MRI
Dual-domain adaptive-scaling non-local network for CT metal artifact reduction
Wang, T., Xia, W., Huang, Y., Sun, H., Liu, Y., Chen, H., Zhou, J., Zhang, Y.
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) (CCF-B) 2021
Strasbourg, France.
Introduces a dual-domain adaptive-scaling non-local network for reducing metal artifacts in CT imaging.
# medical imaging # CT
Dual-domain adaptive-scaling non-local network for CT metal artifact reduction
Wang, T., Xia, W., Huang, Y., Sun, H., Liu, Y., Chen, H., Zhou, J., Zhang, Y.
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) (CCF-B) 2021
Strasbourg, France.
Introduces a dual-domain adaptive-scaling non-local network for reducing metal artifacts in CT imaging.
# medical imaging # CT
DAN-Net: Dual-domain adaptive-scaling non-local network for CT metal artifact reduction
Wang, T., Xia, W., Huang, Y., Sun, H., Liu, Y., Chen, H., Zhou, J., Zhang, Y.
Physics in Medicine & Biology 66(15), 155009 2021
Presents a dual-domain adaptive-scaling non-local network for reducing metal artifacts in computed tomography.
# medical imaging # CT
DAN-Net: Dual-domain adaptive-scaling non-local network for CT metal artifact reduction
Wang, T., Xia, W., Huang, Y., Sun, H., Liu, Y., Chen, H., Zhou, J., Zhang, Y.
Physics in Medicine & Biology 66(15), 155009 2021
Presents a dual-domain adaptive-scaling non-local network for reducing metal artifacts in computed tomography.
# medical imaging # CT
MANAS: Multi-scale and multi-level neural architecture search for low-dose CT denoising
Lu, Z., Xia, W., Huang, Y., Shan, H., Chen, H., Zhou, J., Zhang, Y.
arXiv preprint arXiv:2103.12995 2021
Proposes multi-scale and multi-level neural architecture search for low-dose CT denoising.
# medical imaging # CT # denoising
MANAS: Multi-scale and multi-level neural architecture search for low-dose CT denoising
Lu, Z., Xia, W., Huang, Y., Shan, H., Chen, H., Zhou, J., Zhang, Y.
arXiv preprint arXiv:2103.12995 2021
Proposes multi-scale and multi-level neural architecture search for low-dose CT denoising.
# medical imaging # CT # denoising
2020
Noise-powered disentangled representation for unsupervised speckle reduction of optical coherence tomography images
Huang, Y., Xia, W., Lu, Z., Liu, Y., Chen, H., Zhou, J., Fang, L., Zhang, Y.
IEEE Transactions on Medical Imaging 40(10), pp. 2600-2614 2020
Proposes a noise-powered disentangled representation method for unsupervised speckle reduction in optical coherence tomography images.
# medical imaging # OCT # denoising
Noise-powered disentangled representation for unsupervised speckle reduction of optical coherence tomography images
Huang, Y., Xia, W., Lu, Z., Liu, Y., Chen, H., Zhou, J., Fang, L., Zhang, Y.
IEEE Transactions on Medical Imaging 40(10), pp. 2600-2614 2020
Proposes a noise-powered disentangled representation method for unsupervised speckle reduction in optical coherence tomography images.
# medical imaging # OCT # denoising
Disentanglement network for unsupervised speckle reduction of optical coherence tomography images
Huang, Y., Xia, W., Lu, Z., Liu, Y., Chen, H., Zhou, J., Fang, L., Zhang, Y.
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) (CCF-B) 2020
Lima, Peru.
Introduces an unsupervised disentanglement network for speckle reduction in optical coherence tomography images.
# medical imaging # OCT # denoising
Disentanglement network for unsupervised speckle reduction of optical coherence tomography images
Huang, Y., Xia, W., Lu, Z., Liu, Y., Chen, H., Zhou, J., Fang, L., Zhang, Y.
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) (CCF-B) 2020
Lima, Peru.
Introduces an unsupervised disentanglement network for speckle reduction in optical coherence tomography images.
# medical imaging # OCT # denoising
MD-Recon-Net: a parallel dual-domain convolutional neural network for compressed sensing MRI
Ran, M., Xia, W., Huang, Y., Lu, Z., Bao, P., Liu, Y., Sun, H., Zhou, J., Zhang, Y.
IEEE Transactions on Radiation and Plasma Medical Sciences 5(1), pp. 120-135 2020
Presents a parallel dual-domain convolutional neural network for compressed sensing MRI reconstruction.
# medical imaging # MRI
MD-Recon-Net: a parallel dual-domain convolutional neural network for compressed sensing MRI
Ran, M., Xia, W., Huang, Y., Lu, Z., Bao, P., Liu, Y., Sun, H., Zhou, J., Zhang, Y.
IEEE Transactions on Radiation and Plasma Medical Sciences 5(1), pp. 120-135 2020
Presents a parallel dual-domain convolutional neural network for compressed sensing MRI reconstruction.
# medical imaging # MRI
2019
Simultaneous denoising and super-resolution of optical coherence tomography images based on generative adversarial network
Huang, Y., Lu, Z., Shao, Z., Ran, M., Zhou, J., Fang, L., Zhang, Y.
Optics Express 27(9), pp. 12289-12307 2019
Uses a generative adversarial network to perform simultaneous denoising and super-resolution for optical coherence tomography images.
# medical imaging # OCT # denoising # image generation
Simultaneous denoising and super-resolution of optical coherence tomography images based on generative adversarial network
Huang, Y., Lu, Z., Shao, Z., Ran, M., Zhou, J., Fang, L., Zhang, Y.
Optics Express 27(9), pp. 12289-12307 2019
Uses a generative adversarial network to perform simultaneous denoising and super-resolution for optical coherence tomography images.
# medical imaging # OCT # denoising # image generation