Papers
1
Total Citations
2
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1
About
Min Gan is a leading researcher in medical image analysis and computer-assisted surgery, with a focus on 3D shape reconstruction for minimally-invasive and robot-guided procedures. His most notable contribution is the development of a hierarchical shape-perception network that reconstructs 3D brain models from a single, incomplete 2D image—a breakthrough for navigating surgical environments where direct views are obstructed. This work, published in 2021, has garnered 2 citations and addresses a critical challenge in surgical robotics: generating accurate 3D organ shapes from sparse, indirect visual data. Gan’s research bridges computer vision and clinical practice, enabling safer, more precise interventions by providing surgeons with real-time 3D guidance. His innovative approach to shape perception from limited inputs has the potential to transform pre-operative planning and intra-operative navigation, making complex procedures more accessible. With a growing portfolio, Gan is establishing himself as a key figure in the intersection of deep learning and surgical technology, pushing the boundaries of what is possible in automated surgical assistance.
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Top Papers
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