Papers
2
Total Citations
45
H-Index
2
About
Vibashan VS is a researcher working at the intersection of computer vision, surgical robotics, and deep learning, with a particular focus on developing intelligent systems for image-guided robotic surgery. His most recognized contribution, the Spatio-Temporal Multitask Learning (ST-MTL) model, introduced an innovative framework for simultaneously predicting surgical gaze scanpaths while tracking instruments during robotic procedures — a challenge that demands both spatial precision and temporal reasoning. Published in 2020 and garnering 38 citations, this work demonstrates strong influence within the medical robotics and human-computer interaction communities. Central to Vibashan's research philosophy is the idea of embedding cognitive awareness into automated surgical systems. By modeling task-oriented attention and representation learning, his work aims to reduce the cognitive burden on surgeons, enabling automated camera control that mirrors where an expert's gaze would naturally fall. This has meaningful implications for surgical training, skill assessment, and autonomous assistance in minimally invasive procedures. His research bridges the gap between human expertise and machine intelligence in high-stakes clinical environments, making him a notable contributor to the growing field of AI-assisted surgery and intelligent endoscopic vision systems.
Research Focus
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Top Papers
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