Wenkang Fan

Xiamen University, Xiamen University of Technology

Papers

Total Citations

H-Index

About

Wenkang Fan is a researcher at the forefront of robotic surgery and computer vision, specializing in enhancing surgical perception and autonomy. His work addresses critical challenges in minimally invasive procedures, particularly in vessel localization and 3D scene understanding. Fan’s most cited paper, “Robotically Surgical Vessel Localization Using Robust Hybrid Video Motion Magnification” (2021, 10 citations), introduces a novel method to amplify subtle visual motions in endoscopic video, enabling surgeons to identify hidden vessels and neurovascular bundles—a task traditionally limited by poor visual and tactile feedback. This contribution directly improves surgical safety by reducing inadvertent injuries. More recently, Fan proposed the “Densely Convolved Transformer Aggregation Networks (DCTAN)” for monocular dense depth prediction in robotic endoscopy (2024). This work tackles the difficult problem of reconstructing 3D surgical fields from single-camera views, overcoming challenges like narrow fields of view and uneven illumination. By combining convolutional and transformer architectures, DCTAN achieves precise depth estimation, which is vital for expanding the surgeon’s situational awareness. With a growing citation impact, Fan’s research is paving the way for smarter, safer robotic-assisted surgeries, making him a promising voice in surgical AI.