Honson Tran
Papers
1
Total Citations
11
H-Index
1
About
Honson Tran is a researcher at the forefront of integrating artificial intelligence with minimally invasive surgery, with a particular focus on catheter-based interventions. His work centers on developing deep learning methods to enhance surgical navigation and visualization, especially in complex cardiac procedures. Tran’s most cited paper, "Deep learning-driven catheter tracking from bi-plane X-ray fluoroscopy of 3D printed heart phantoms" (2021, 11 citations), demonstrates his innovative approach to improving the accuracy and safety of catheter guidance using AI. By combining 3D-printed anatomical models with neural networks, he has created a framework that could significantly reduce radiation exposure and improve procedural outcomes. This work is part of a broader effort to expand the capabilities of robotic surgical systems and navigation technologies, addressing the growing demand for more precise, less invasive treatments. Tran’s contributions are particularly valuable for students and researchers interested in the intersection of computer vision, medical imaging, and surgical robotics, offering a clear example of how deep learning can solve real-world clinical challenges.
Research Focus
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Top Papers
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