Shengqian Zhu / Academic Homepage

Academic Homepage

Welcome — this is the academic page of Shengqian Zhu

My research interests include artificial intelligence, computer vision, and intelligent medicine, with a particular focus on medical image segmentation, continual learning, visual prompts, and radiotherapy-oriented image analysis.

10Published / accepted papers
7First-author papers
3CCF-A papers

News

Selected publication updates.

  1. Deconstructing Supervision: Rethinking Learning Signals in Class Incremental Medical Image Segmentation was accepted by ACM Multimedia 2026.

  2. Class Incremental Medical Image Segmentation via Prototype-Guided Calibration and Dual-Aligned Distillation was published in AAAI 2026.

  3. PRIME: Prototype-Driven Class Incremental Learning for Medical Image Segmentation was published in ACM Multimedia 2025.

  4. Visual prompt-driven universal model for medical image segmentation in radiotherapy was published in Knowledge-Based Systems.

Education

Academic background.

Sichuan University, Ph.D. candidate in Computer Science and Technology

Research interests: artificial intelligence, computer vision, and intelligent medicine. Supervisor: Prof. Zhang Yi.

Sichuan University, B.Eng. in Computer Science and Technology

B.Eng. in Computer Science and Technology.

Research Interests

Research topics suitable for a public academic homepage.

01

Medical image segmentation

Universal, interactive, and radiotherapy-specific segmentation for organs-at-risk, targets, and clinical regions of interest.

02

Continual and incremental learning

Prototype-guided calibration, dual-aligned distillation, and class-incremental segmentation for expanding medical image tasks.

03

Visual prompt and model adaptation

Prompt-driven universal models and task-consistency learning for versatile medical image segmentation.

Publications

Published / accepted papers: 10. Under review: 1. First-author papers: 7.

2026

  1. Shengqian Zhu, Xiaogang Xu, Wenbo Qi, Chengrong Yu, Qiang Wang, Jun Wu, Jiafei Wu, Zhang Yi, Junjie Hu. Deconstructing Supervision: Rethinking Learning Signals in Class Incremental Medical Image Segmentation. ACM Multimedia (ACM MM), 2026. Accepted. CCF-A
  2. Shengqian Zhu, Chengrong Yu, Qiang Wang, Ying Song, Guangjun Li, Jiafei Wu, Xiaogang Xu, Zhang Yi, Junjie Hu. Class Incremental Medical Image Segmentation via Prototype-Guided Calibration and Dual-Aligned Distillation. Proceedings of the AAAI Conference on Artificial Intelligence, 40(16): 13961-13969, 2026. CCF-A
  3. Blöcker T. J., Görts P. A. W., Wang Y., et al. MRIgRT real-time target tracking: TrackRAD2025 challenge report. Medical Image Analysis, 104134, 2026.

2025

  1. Shengqian Zhu, Chengrong Yu, Wenbo Qi, Jiafei Wu, Ying Song, Guangjun Li, Zhang Yi, Xiaogang Xu, Junjie Hu. PRIME: Prototype-Driven Class Incremental Learning for Medical Image Segmentation. Proceedings of the 33rd ACM International Conference on Multimedia, 4688-4697, 2025. CCF-A
  2. Shengqian Zhu, Jiafei Wu, Xiaogang Xu, Chengrong Yu, Ying Song, Zhang Yi, Guangjun Li, Junjie Hu. Exploiting Unlabeled Structures through Task Consistency Training for Versatile Medical Image Segmentation. arXiv preprint arXiv:2509.04732, 2025.
  3. Shengqian Zhu, Yuncheng Shen, Yingyong Yin, Ying Song, Zhang Yi, Guangjun Li, Junjie Hu. Rethinking Propagation Methods for Interactive Medical Image Segmentation. IEEE Journal of Biomedical and Health Informatics, 2025.
  4. Shengqian Zhu, Chengrong Yu, Zhang Yi, Junjie Hu. Visual prompt-driven universal model for medical image segmentation in radiotherapy. Knowledge-Based Systems, 326: 114006, 2025.

2024 and Earlier

  1. Shengqian Zhu, Chengrong Yu, Junjie Hu. Regularizing deep neural networks for medical image analysis with augmented batch normalization. Applied Soft Computing, 154: 111337, 2024.
  2. Chengrong Yu, Ying Song, Qiang Wang, Shengqian Zhu, Zhang Yi, Junjie Hu. Leveraging denoising diffusion probabilistic model to improve the multi-thickness CT segmentation. Neurocomputing, 610: 128573, 2024.
  3. Junjie Hu, Chengrong Yu, Shengqian Zhu, Haixian Zhang. Incorporating Adaptive Sparse Graph Convolutional Neural Networks for Segmentation of Organs at Risk in Radiotherapy. International Journal of Intelligent Systems, 2024(1): 1728801, 2024.
  4. Chengrong Yu, Junjie Hu, Guiyuan Li, Shengqian Zhu, Sen Bai, Zhang Yi. Segmentation for regions of interest in radiotherapy by self-supervised learning. Knowledge-Based Systems, 256: 109370, 2022.

Contact

Public academic links.

Email: ZhuShengqianSCU@gmail.com · Google Scholar: profile · GitHub: shengqianzhu