Li Lab for Intelligent Imaging-College of Al, Tsinghua University

Research

Li Lab for Intelligent Imaging

PI：Xinyang LI

Research Direction: Imaging and vision, AI-empowered scientific observation

Research Group Introduction

As an extension of human vision, imaging instruments have expanded our observational capability to scales and precisions beyond the reach of the naked eye, leading to a series of scientific discoveries. To solve fundamental challenges in imaging, our group is committed to empowering scientific observation with AI to push the boundaries of observational performance, thereby accelerating scientific discovery across multiple disciplines. Our research directions include:

Intelligent Imaging and Image Analysis: Investigating high-performance, interpretable, and self-supervised image analysis methods. Exploring new architectures that integrate imaging with AI to push the boundaries of scientific observation and promote new scientific discoveries.
Foundation Models for Scientific Observation: Building multi-scale, cross-modality, and high-quality image datasets. Developing foundation models for frontier research to achieve accurate representation and mechanistic understanding of complex phenomena.
Advanced Imaging Mechanisms and Systems: Combining imaging with robotics for autonomous perception and active observation. Exploring new paradigms that integrate quantum effects with optical imaging, aiming to break the fundamental limits of classical systems.

Representative Publications

[1] Yixin Li, Qi Zhang, Yuanlong Zhang, Jiaqi Fan, Zhi Lu, Xinhong Xu, Xinyang Li#, et al. Unsupervised transfer learning enables multi-animal tracking without training annotation, Nature Methods, 2026.

[2] Xinyang Li*, Yixin Li*, Yiliang Zhou, Jiamin Wu, et al. "Real-time denoising enables high-sensitivity fluorescence time-lapse imaging beyond the shot-noise limit." Nature Biotechnology, 2023.

[3] Xinyang Li*, Yuanlong Zhang*, Jiamin Wu#, Qionghai Dai#. "Challenges and opportunities in bioimage analysis." Nature Methods, 2023.

[4] Xinyang Li*, Guoxun Zhang*, Jiamin Wu, Yuanlong Zhang, et al. "Reinforcing neuron extraction and spike inference in calcium imaging using deep self-supervised denoising." Nature Methods, 2021.

[5] Xinyang Li*, Xiaowan Hu*, Xingye Chen*, Jiaqi Fan, et al. "Spatial redundancy transformer for self-supervised fluorescence image denoising." Nature Computational Science, 2023.

[6] Xinyang Li*, Guoxun Zhang*, Hui Qiao*, Feng Bao, et al. Unsupervised content-preserving transformation for optical microscopy. Light: Science & Applications, 2021.

[7] Zhi Lu*, Wentao Chen*, Feihao Sun, Jiaqi Fan, Xinyang Li, et al. Leveraging spatial-angular redundancy for self-supervised denoising of 3D fluorescence imaging without temporal dependency. Nature Communications, 2025.

[8] Zhifeng Zhao*, Yiliang Zhou*, Bo Liu*, Jing He*, Jiayin Zhao, Yeyi Cai, Jingtao Fan, Xinyang Li, et al. "Two-photon synthetic aperture microscopy for minimally invasive fast 3D imaging of native subcellular behaviors in deep tissue." Cell, 2023.

[9] Guoxun Zhang*, Xiaopeng Li*, Yuanlong Zhang*, Xiaofei Han, Xinyang Li, et al. Bio-friendly long-term subcellular dynamic recording by self-supervised image enhancement microscopy. Nature Methods, 2023.

[10] Yuanlong Zhang*, Guoxun Zhang*, Xiaofei Han, Jiamin Wu, Ziwei Li, Xinyang Li, et al. Rapid detection of neurons in widefield calcium imaging datasets after training with synthetic data. Nature Methods, 2023.