Papers
1
Total Citations
2
H-Index
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About
Feng Ju is an emerging researcher working at the intersection of surgical robotics, medical image analysis, and computer-assisted interventions. Their most notable recent contribution, "RASEC: Rescaling Acquisition Strategy with Energy Constraints under Fusion Kernel for Active Incision Recommendation in Tracheotomy" (2024), addresses a critical challenge in airway surgery — the precise placement of incisions during tracheotomy procedures. By developing an intelligent acquisition strategy that incorporates energy constraints and fusion kernel techniques, Ju's work aims to reduce complication risks in a procedure commonly performed on patients requiring prolonged intubation, airway obstruction management, or neck trauma care. This research represents a meaningful step toward augmenting surgical decision-making with data-driven recommendations, potentially improving patient safety in high-stakes clinical environments. Though early in their citation trajectory — with 2 citations to date — Ju's focus on active learning frameworks applied to surgical guidance reflects a growing and impactful niche within medical AI. Students and researchers interested in human-computer interaction in the operating room, intelligent surgical planning, or image-guided interventions will find Ju's work a valuable reference point for where clinical AI innovation is heading.
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Top Papers
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