1 citations · 4 across the 19 of their papers we have counts for
22 papers
Complexity-Aware Surgical Video Reasoning Segmentation with Digital Twin and Context Memory
Yiqing Shen, Mathias Unberath
Conventional surgical segmentation relies on closed-set categories or explicit text queries, which cannot resolve implicit queries that require reasoning about workflows, instrumen…
2D Versus 3D Diffusion for In Silico Training of Interventional X-ray AI Models
Sampath Rapuri, Jeremy Ko, Benjamin D. Killeen +2
The ability to synthesize realistic X-ray images has catalyzed the development of AI models for X-ray image-guided procedures, which otherwise suffer from a lack of available annot…
Reasoning Text-to-Video Retrieval for Operating Room Clips via Action-Driven Digital Twins
Yiqing Shen, Hao Ding, Mathias Unberath
Text-to-video retrieval in operating rooms (OR) is an enabling technology for OR safety, as it allows stakeholders to retrieve and inspect recordings of specific events. However, b…
Training LLMs with Reinforcement Learning over Digital Twin Representations for Reasoning-Intensive Surgical VideoQA
Yiqing Shen, Han Zhang, Mathias Unberath
Surgical video question answering requires multi-step reasoning across semantic, spatial, and temporal dimensions. Existing methods architecturally compress videos into discrete to…
Towards Controllable Video Synthesis of Routine and Rare OR Events
Dominik Schneider, Lalithkumar Seenivasan, Sampath Rapuri +8
Purpose: Curating large-scale datasets of operating room (OR) workflow, encompassing rare, safety-critical, or atypical events, remains operationally and ethically challenging. Thi…
Counterfactual World Models via Digital Twin-conditioned Video Diffusion
Yiqing Shen, Aiza Maksutova, Chenjia Li +1
World models learn to predict the temporal evolution of visual observations given a control signal, potentially enabling agents to reason about environments through forward simulat…
Text-Driven Reasoning Video Editing via Reinforcement Learning on Digital Twin Representations
Yiqing Shen, Chenjia Li, Mathias Unberath
Text-driven video editing enables users to modify video content only using text queries. While existing methods can modify video content if explicit descriptions of editing targets…
Reasoning Text-to-Video Retrieval via Digital Twin Video Representations and Large Language Models
Yiqing Shen, Chenxiao Fan, Chenjia Li +1
The goal of text-to-video retrieval is to search large databases for relevant videos based on text queries. Existing methods have progressed to handling explicit queries where the…
Fast Reasoning Segmentation for Images and Videos
Yiqing Shen, Mathias Unberath
Reasoning segmentation enables open-set object segmentation via implicit text queries, therefore serving as a foundation for embodied agents that should operate autonomously in rea…
Constructing and Interpreting Digital Twin Representations for Visual Reasoning via Reinforcement Learning
Yiqing Shen, Mathias Unberath
Visual reasoning may require models to interpret images and videos and respond to implicit text queries across diverse output formats, from pixel-level segmentation masks to natura…
TwinOR: Photorealistic Digital Twins of Dynamic Operating Rooms for Embodied AI Research
Han Zhang, Yiqing Shen, Roger D. Soberanis-Mukul +11
Developing embodied AI for intelligent surgical systems requires safe, controllable environments for continual learning and evaluation. However, safety regulations and operational…
Temporally-Constrained Video Reasoning Segmentation and Automated Benchmark Construction
Yiqing Shen, Chenjia Li, Chenxiao Fan +1
Conventional approaches to video segmentation are confined to predefined object categories and cannot identify out-of-vocabulary objects, let alone objects that are not identified…
Reasoning Segmentation for Images and Videos: A Survey
Yiqing Shen, Chenjia Li, Fei Xiong +4
Reasoning Segmentation (RS) aims to delineate objects based on implicit text queries, the interpretation of which requires reasoning and knowledge integration. Unlike the tradition…
RVTBench: A Benchmark for Visual Reasoning Tasks
Yiqing Shen, Chenjia Li, Chenxiao Fan +1
Visual reasoning, the capability to interpret visual input in response to implicit text query through multi-step reasoning, remains a challenge for deep learning models due to the…
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…
Online Reasoning Video Segmentation with Just-in-Time Digital Twins
Yiqing Shen, Bohan Liu, Chenjia Li +2
Reasoning segmentation (RS) aims to identify and segment objects of interest based on implicit text queries. As such, RS is a catalyst for embodied AI agents, enabling them to inte…
Operating Room Workflow Analysis via Reasoning Segmentation over Digital Twins
Yiqing Shen, Chenjia Li, Bohan Liu +3
Analyzing operating room (OR) workflows to derive quantitative insights into OR efficiency is important for hospitals to maximize patient care and financial sustainability. Prior w…
An Intrinsically Explainable Approach to Detecting Vertebral Compression Fractures in CT Scans via Neurosymbolic Modeling
Blanca Inigo, Yiqing Shen, Benjamin D. Killeen +4
Vertebral compression fractures (VCFs) are a common and potentially serious consequence of osteoporosis. Yet, they often remain undiagnosed. Opportunistic screening, which involves…
Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer
Xinyuan Shao, Yiqing Shen, Mathias Unberath
Segment Anything Models (SAMs) have gained increasing attention in medical image analysis due to their zero-shot generalization capability in segmenting objects of unseen classes a…
Promptable Counterfactual Diffusion Model for Unified Brain Tumor Segmentation and Generation with MRIs
Yiqing Shen, Guannan He, Mathias Unberath
Brain tumor analysis in Magnetic Resonance Imaging (MRI) is crucial for accurate diagnosis and treatment planning. However, the task remains challenging due to the complexity and v…
FastSAM-3DSlicer: A 3D-Slicer Extension for 3D Volumetric Segment Anything Model with Uncertainty Quantification
Yiqing Shen, Xinyuan Shao, Blanca Inigo Romillo +2
Accurate segmentation of anatomical structures and pathological regions in medical images is crucial for diagnosis, treatment planning, and disease monitoring. While the Segment An…
FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Images
Yiqing Shen, Jingxing Li, Xinyuan Shao +4
Segment anything models (SAMs) are gaining attention for their zero-shot generalization capability in segmenting objects of unseen classes and in unseen domains when properly promp…