Papers
1
Total Citations
36
H-Index
1
About
Yirou Pan is a researcher at the forefront of medical image processing, with a primary focus on enhancing visual clarity in robotic-assisted laparoscopic surgery. Her most cited work, "DeSmoke-LAP: improved unpaired image-to-image translation for desmoking in laparoscopic surgery" (2022, 36 citations), addresses a critical challenge in minimally invasive procedures: smoke generated by electrocauterization that severely degrades surgeon visibility. Pan’s major contribution lies in developing an advanced unpaired image-to-image translation framework that effectively removes surgical smoke without requiring paired training data, a significant improvement over prior methods. This innovation directly supports safer, more efficient robotic surgeries by restoring clear visualization in real-time. Her work has garnered attention for its practical impact on surgical outcomes, with citations reflecting its relevance to both computer vision and clinical applications. Pan’s research bridges the gap between deep learning and operative medicine, offering a tangible solution to a persistent intraoperative problem. Her achievements underscore a commitment to translating computational methods into tools that enhance surgical precision and patient safety.
Research Focus
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Top Papers
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