Papers
6
Total Citations
95
H-Index
5
About
Yidan Qin is a researcher specializing in robot-assisted surgery (RAS), with a focus on surgical state estimation, temporal segmentation, and motion prediction using deep learning techniques. Their work addresses one of the central challenges in surgical automation: enabling robotic systems to perceive, interpret, and anticipate complex surgical workflows in real time. Qin's most cited contribution, "Temporal Segmentation of Surgical Sub-tasks through Deep Learning with Multiple Data Sources" (2020, 38 citations), demonstrated how finite-state machine representations of surgical tasks could be decoded through multi-modal deep learning, laying foundational groundwork for autonomous surgical systems. Building on this, their "daVinciNet" framework (2020, 26 citations) advanced the field by jointly predicting both instrument trajectories and surgical states — a critical prerequisite for shared control between surgeons and robotic systems. Their subsequent research has pushed toward greater robustness and scalability. Work on hierarchical surgical state estimation (2021, 12 citations) introduced multi-granularity temporal modeling, while research on invariant task representations (2021, 7 citations) tackled the practical challenge of generalizing across different surgeons and datasets. Collectively, Qin's contributions meaningfully advance the automation of robot-assisted surgery, with cumulative citations reflecting growing recognition within the surgical robotics community.
Research Focus
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