collaborators

19 papers

cs.CV2026

Dense Structural Priors for Sparse Functional Landmark Localization in Surgical Videos

Chenyan Jing, Hao Ding, Lalithkumar Seenivasan +2

Vision foundation models such as SAM 3 can provide transferable object-level structure across diverse surgical video conditions, but segmentation outputs do not explicitly encode t…

cs.CV2026

C3VD-DEFCOL: A Deformable Colonoscopy Dataset with Time-Resolved 3D Ground Truth and Realistic Appearance

Ethan Luk, Mayank V. Golhar, Anthony Song +5

3D reconstruction could improve colonoscopy by estimating mucosal coverage and alerting clinicians to missed regions during screening. However, algorithm development is limited as…

cs.RO2026

Open-H-Embodiment: A Large-Scale Dataset for Enabling Foundation Models in Medical Robotics

Open-H-Embodiment Consortium, :, Nigel Nelson +213

Autonomous medical robots hold promise to improve patient outcomes, reduce provider workload, democratize access to care, and enable superhuman precision. However, autonomous medic…

cs.CV2026

Investigating Robot Control Policy Learning for Autonomous X-ray-guided Spine Procedures

Florence Klitzner, Blanca Inigo, Benjamin D. Killeen +4

Imitation learning-based robot control policies are enjoying renewed interest in video-based robotics. However, it remains unclear whether this approach applies to X-ray-guided pro…

cs.RO2026

Towards Robust Surgical Automation via Digital Twin Representations from Foundation Models

Hao Ding, Lalithkumar Seenivasan, Hongchao Shu +7

Large language model-based (LLM) agents are emerging as a powerful enabler of robust embodied intelligence due to their capability of planning complex action sequences. Sound plann…

cs.CV2026

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge

Hao Ding, Yuqian Zhang, Tuxun Lu +39

Surgical data science has seen rapid advancement with the excellent performance of end-to-end deep neural networks (DNNs). Despite their successes, DNNs have been proven susceptibl…