2 citations · 4 across the 13 of their papers we have counts for
14 papers
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…
TikArt: Stabilizing Aperture-Guided Fine-Grained Visual Reasoning with Reinforcement Learning
Hao Ding, Zhichuan Yang, Weijie Ge +3
Fine-grained visual reasoning in multimodal large language models (MLLMs) is bottlenecked by single-pass global image encoding: key evidence often lies in tiny objects, cluttered r…
BronchOpt : Vision-Based Pose Optimization with Fine-Tuned Foundation Models for Accurate Bronchoscopy Navigation
Hongchao Shu, Roger D. Soberanis-Mukul, Jiru Xu +6
Accurate intra-operative localization of the bronchoscope tip relative to patient anatomy remains challenging due to respiratory motion, anatomical variability, and CT-to-body dive…
Egosurg: Arbitrary view synthesis for egocentric replay of operating room workflows from ambient cameras
Han Zhang, Lalithkumar Seenivasan, Jose L. Porras +10
Observing surgical practice has historically relied on fixed vantage points or recollections, leaving the egocentric perspectives that shape clinical decisions undocumented. Ambien…
Beyond Rigid AI: Towards Natural Human-Machine Symbiosis for Interoperative Surgical Assistance
Lalithkumar Seenivasan, Jiru Xu, Roger D. Soberanis Mukul +6
Emerging surgical data science and robotics solutions, especially those designed to provide assistance in situ, require natural human-machine interfaces to fully unlock their poten…
Position: Foundation Models Need Digital Twin Representations
Yiqing Shen, Hao Ding, Lalithkumar Seenivasan +2
Current foundation models (FMs) rely on token representations that directly fragment continuous real-world multimodal data into discrete tokens. They limit FMs to learning real-wor…
Endo3R: Unified Online Reconstruction from Dynamic Monocular Endoscopic Video
Jiaxin Guo, Wenzhen Dong, Tianyu Huang +5
Reconstructing 3D scenes from monocular surgical videos can enhance surgeon's perception and therefore plays a vital role in various computer-assisted surgery tasks. However, achie…
UINO-FSS: Unifying Representation Learning and Few-shot Segmentation via Hierarchical Distillation and Mamba-HyperCorrelation
Wei Zhuo, Zhiyue Tang, Wufeng Xue +3
Few-shot semantic segmentation has attracted growing interest for its ability to generalize to novel object categories using only a few annotated samples. To address data scarcity,…
dARt Vinci: Egocentric Data Collection for Surgical Robot Learning at Scale
Yihao Liu, Yu-Chun Ku, Jiaming Zhang +3
Data scarcity has long been an issue in the robot learning community. Particularly, in safety-critical domains like surgical applications, obtaining high-quality data can be especi…
MoSFormer: Augmenting Temporal Context with Memory of Surgery for Surgical Phase Recognition
Hao Ding, Xu Lian, Mathias Unberath
Surgical phase recognition from video enables various downstream applications. Transformer-based sliding window approaches have set the state-of-the-art by capturing rich spatial-t…
Towards Robust Algorithms for Surgical Phase Recognition via Digital Twin Representation
Hao Ding, Yuqian Zhang, Wenzheng Cheng +7
Surgical phase recognition (SPR) is an integral component of surgical data science, enabling high-level surgical analysis. End-to-end trained neural networks that predict surgical…
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…
Performance and Non-adversarial Robustness of the Segment Anything Model 2 in Surgical Video Segmentation
Yiqing Shen, Hao Ding, Xinyuan Shao +1
Fully supervised deep learning (DL) models for surgical video segmentation have been shown to struggle with non-adversarial, real-world corruptions of image quality including smoke…
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…