Research
Li Lab for Intelligent Imaging
PI：Xinyang LI
Research Direction: Advancing scientific observation with AI to accelerate scientific discovery
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. Aiming to solving cutting-edge problems in the field of imaging, our group is committed to advancing scientific observation with AI. Through innovations in imaging mechanisms, system architectures, and intelligent algorithms, we are pushing forward the boundaries of observational performance and intersecting with multiple disciplines to accelerate scientific discovery. Our ongoing work includes:
Intelligent Imaging: Investigating high-performance, interpretable, and self-supervised intelligent bioimage analysis methods. Exploring new architectures that integrate optical imaging with AI to break through classical observational limits and promote new scientific discoveries.
Neural Imaging Robot: Combining imaging with robotics for neural recording in freely behaving animals. This new architecture will become an indispensable technology for revealing the neural mechanism of complex behaviors, and providing technical support for the advancement of brain science and AI.
Quantum Imaging and Quantum-Enhanced Optical Computing: Exploring new paradigms that integrate quantum effects with optical imaging. Designing and building quantum imaging systems to break the standard quantum limit. As an extension, we will empower optical computing with quantum imaging to achieve high-throughput and high-precision photonic AI computing beyond the standard quantum limit.
Representative Publications
[1] Xinyang Li, Yixin Li, Yiliang Zhou, Jiamin Wu, Zhifeng Zhao, Jiaqi Fan, Fei Deng, Zhaofa Wu, Guihua Xiao, Jing He, Yuanlong Zhang, Guoxun Zhang, Xiaowan Hu, Xingye Chen, Yi Zhang, Hui Qiao, Hao Xie, Yulong Li, Haoqian Wang, Lu Fang, Qionghai Dai. "Real-time denoising enables high-sensitivity fluorescence time-lapse imaging beyond the shot-noise limit." Nature Biotechnology (2023): 282-292.
[2] Xinyang Li, Guoxun Zhang, Jiamin Wu, Yuanlong Zhang, Zhifeng Zhao, Xing Lin, Hui Qiao, Hao Xie, Haoqian Wang, Lu Fang, and Qionghai Dai. "Reinforcing neuron extraction and spike inference in calcium imaging using deep self-supervised denoising." Nature Methods (2021): 1395-1400.
[3] Xinyang Li, Yuanlong Zhang, Jiamin Wu, Qionghai Dai. "Challenges and opportunities in bioimage analysis." Nature Methods (2023): 958-961.
[4] Xinyang Li, Xiaowan Hu, Xingye Chen, Jiaqi Fan, Zhifeng Zhao, Jiamin Wu, Haoqian Wang, Qionghai Dai. "Spatial redundancy transformer for self-supervised fluorescence image denoising." Nature Computational Science (2023): 1067-1080.
[5] Xinyang Li, Guoxun Zhang, Hui Qiao, Feng Bao, Yue Deng, Jiamin Wu, Yangfan He, Jingping Yun, Xing Lin, Hao Xie, Haoqian Wang, Qionghai Dai. (2021). Unsupervised content-preserving transformation for optical microscopy. Light: Science & Applications (2021): 44-54.
[6] Zhifeng Zhao, Yiliang Zhou, Bo Liu, Jing He, Jiayin Zhao, Yeyi Cai, Jingtao Fan, Xinyang Li, Zilin Wang, Zhi Lu, Jiamin Wu, Hai Qi, Qionghai Dai. "Two-photon synthetic aperture microscopy for minimally invasive fast 3D imaging of native subcellular behaviors in deep tissue." Cell (2023): 2475-2491 e22.
[7] Guoxun Zhang, Xiaopeng Li, Yuanlong Zhang, Xiaofei Han, Xinyang Li, Jinqiang Yu, Boqi Liu, Jiamin Wu, Li Yu, Qionghai Dai. Bio-friendly long-term subcellular dynamic recording by self-supervised image enhancement microscopy. Nature Methods (2023): 1-14.
[8] Yuanlong Zhang, Guoxun Zhang, Xiaofei Han, Jiamin Wu, Ziwei Li, Xinyang Li, Guihua Xiao, Hao Xie, Lu Fang, Qionghai Dai. Rapid detection of neurons in widefield calcium imaging datasets after training with synthetic data. Nature Methods (2023): 747-754.
[9] Yi Zhang, Yuling Wang, Mingrui Wang, Yuduo Guo, Xinyang Li, Yifan Chen, Zhi Lu, Jiamin Wu, Xiangyang Ji, Qionghai Dai. "Multi-focus light-field microscopy for high-speed large-volume imaging." PhotoniX (2022): 1-20.
[10] Xinyang Li, Yuanlong Zhang, Kan Liu, Hao Xie, Haoqian Wang, Lingjie Kong, Qionghai Dai. "Adaptive optimization for axial multi-foci generation in multiphoton microscopy." Optics express (2019): 35948-35961.
Group Members
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Jiachen XIE
xiejc20@mails.tsinghua.edu.cn
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Yixin LI
liyixin318@gmail.com
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Tieyu WANG
wtr24@mails.tsinghua.edu.cn
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Zijin XU
xuzijinganny@gmail.com
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Xirou ZHOU
zhouxrxd@163.com
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Yutong GU
gyt04082888@163.com