Education
Experience
About Me
I am a Ph.D. student in Cyber Science and Engineering at Sichuan University. My research focuses on federated learning, image generation, and AI for healthcare.
I am especially interested in representation learning for distributed, privacy-sensitive, and clinically complex settings: how models can learn from heterogeneous clients, preserve useful local knowledge, and still build robust global representations. My recent work spans federated visual learning, reliable multi-label medical image diagnosis, and LLM-orchestrated CT reconstruction, while my earlier work studied deep learning methods for MRI reconstruction, CT reconstruction and denoising, CT metal artifact reduction, and OCT speckle reduction.
Before starting my doctoral study, I worked as a Software Engineer at Huawei Cloud Computing Technology Co., Ltd., where I gained practical experience in cloud computing, large-scale distributed systems, and data analysis.
For collaboration, feel free to reach me at yqhuang2912@gmail.com.
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Publication Highlights
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
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
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
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