Zhaoshuo Li

Johns Hopkins University

Papers

12

Total Citations

218

H-Index

8

About

Zhaoshuo Li is a pioneering researcher at the intersection of surgical robotics, medical imaging, and computer vision. His work centers on developing intelligent systems that enhance surgical precision and safety, with key contributions in dynamic surgical scene reconstruction, automated anatomical segmentation, and robot-assisted intervention. Li’s most impactful work, “E-DSSR,” introduces a transformer-based stereoscopic depth perception method for efficient dynamic surgical scene reconstruction (56 citations), enabling real-time 3D understanding during procedures. In neurotologic surgery, his automated registration-based segmentation of temporal bone CT imaging (36 citations) and self-configuring deep learning network (29 citations) have streamlined preoperative planning, reducing manual effort and improving surgical safety. More recently, Li has advanced vision-language-action models with “CoT-VLA,” integrating visual chain-of-thought reasoning for generalizable robot control (23 citations). His hybrid robot-assisted frameworks for retinal endomicroscopy (22 citations) and anatomical mesh-based virtual fixtures for surgical robots (16 citations) further demonstrate his impact on minimally invasive techniques. With over 200 total citations, Li’s work bridges perception, automation, and clinical application, making him a leading voice in next-generation surgical technology.

Research Focus

Key Achievements

8
H-Index
12
Papers
218
Total Citations
18
Avg Citations/Paper
🏆 Most Cited Paper
E-DSSR: Efficient Dynamic Surgical Scene Reconstruction with Transformer-Based Stereoscopic Depth Perception
56 citations · 2021
📈 Most Prolific Year: 2020 (4 Papers)
🤝 Key Collaborators: 54
🏛 Institutions: Johns Hopkins University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago