Papers
43
Total Citations
997
H-Index
17
About
Pheng-Ann Heng is a prominent researcher at the forefront of computer-assisted surgery, medical image analysis, and surgical robotics. His work has fundamentally advanced the integration of artificial intelligence into minimally invasive and robot-assisted surgical systems, with particular focus on instrument segmentation, surgical workflow analysis, and autonomous robotic learning. Among his most influential contributions is pioneering work on surgical instrument segmentation from endoscopic video, leveraging temporal motion flows to improve accuracy in complex intraoperative environments — a paper that has garnered over 120 citations. His team's development of SurRoL, an open-source reinforcement learning platform compatible with the da Vinci Research Kit, has become a landmark resource enabling the broader research community to advance surgical robot autonomy. Heng has also led and contributed to major benchmark challenges, including ROBUST-MIS 2019, shaping community-wide standards for instrument segmentation evaluation. His research extends into surgical gesture recognition using graph-based relational learning and tree search methods, as well as sim-to-real frameworks for industrial robotic manipulation. With collectively hundreds of citations across his recent publications, Heng's interdisciplinary contributions bridge computer vision, reinforcement learning, and clinical robotics, making his work essential reading for anyone entering the field of intelligent surgical systems.
Research Focus
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