Papers
4
Total Citations
35
H-Index
2
About
Sharib Ali is a leading researcher in computer vision for minimally invasive surgery, with a focus on real-time instrument segmentation and detection in endoscopic procedures. His work addresses critical challenges in computer- and robotic-assisted surgery, aiming to enhance surgeon safety and enable autonomous robotic systems. Ali’s most cited paper, “Assessing YOLACT++ for real time and robust instance segmentation of medical instruments in endoscopic procedures” (2021, 24 citations), introduced a state-of-the-art approach for image-based tracking of laparoscopic tools, contributing to the Robust Medical Instrument Segmentation (ROBUST-MIS) Challenge. He further advanced the field with “Real‐time surgical tool detection with multi‐scale positional encoding and contrastive learning” (2023, 7 citations), improving detection accuracy for surgical training and robotic autonomy. His earlier work on the “Improved SIFT algorithm for place categorization” (2015) demonstrates a foundation in feature extraction and computational efficiency. Through these contributions, Ali has helped push the boundaries of real-time, robust computer vision in surgical settings, making his research highly relevant for students and engineers developing next-generation medical technologies.
Research Focus
Key Achievements
Top Papers
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- 4Improved SIFT algorithm for place categorization2 citations · 2015