1 citations · 1 across the 40 of their papers we have counts for
44 papers
PathPilot: An Adaptive Question-Conditioned Multi-Magnification Navigation Agent for Whole-Slide Image Analysis
Zhengyang Xu, Han Li, Jingsong Liu +5
The diagnostic capability of WSI agents relies on effective slide navigation to identify diagnostically relevant regions and efficiently integrate evidence across the slide. While…
Scanning While Imagining: A Scene-Graph World Model for Robotic Ultrasound Navigation
Xuesong Li, Shuai Chen, Feng Li +3
Ultrasound (US) acquisition depends on the operator's ability to interpret anatomy and anticipate how the view will change with probe motion. Many robotic US navigation methods sel…
A Surgical Foundation Model Reveals Task-Dependent Label Efficiency
Florian Philipp Stilz, Lorenzo Arboit, Vinkle Srivastav +6
Developing label-efficient models is a central challenge in surgical AI due to the high cost and scarcity of expert annotation. While self-supervised foundation models adapt well t…
SOS! : A Streamlined Object-Conditional Transformer for Model-free Segmentation
Jiaqi Hu, Junwen Huang, Hongli Xu +4
Foundation segmentation models excel at generating high-quality, class-agnostic masks, but they struggle to associate these proposals with specific target objects. This semantic ga…
Policy-Driven CT-Agent: Modeling Phase-Aware Diagnostic Control for Clinically Consistent CT Reasoning
Yanmeng Dong, Han Li, Yujia Li +7
Computed Tomography (CT) diagnosis often relies on dynamic selection of imaging phases, such as non-contrast, arterial, or venous phases, based on preliminary findings, clinical su…
HyperVLP: Enhancing Hierarchical Surgical Video-Language Pre-training in Hyperbolic Space
Yaojun Hu, Kun Yuan, Nassir Navab +3
Surgical vision-language foundation models typically adopt educational materials, such as surgical lecture videos, to transfer surgical knowledge encoded in language into visual re…
Re-mixing Embeddings for Patient Augmentation in Data Scarce Multiple Instance Learning
Muhammed Furkan Dasdelen, Fatih Ozlugedik, Anastasia Litinetskaya +3
Data scarcity is a major bottleneck in medical Multiple Instance Learning (MIL), especially for rare diseases or expensive modalities. We introduce a statistically grounded patient…
Pose Anything Anywhere:Model-free Object Poses from Arbitrary References
Hongli Xu, Jiaqi Hu, Junwen Huang +5
Estimating the 6D pose of unseen objects is a fundamental yet challenging problem for open-world robotics and embodied perception. Model-based methods are accurate but depend on CA…
P-JEPA: Procedural Video Representation Learning via Joint Embedding Predictive Architecture
Felix Tristram, Stefano Gasperini, Benjamin Killeen +4
The increasing maturity of embodied AI platforms has driven a growing interest in procedural video representation learning to support intelligent assistance systems for complex, mu…
Prompting Diffusion Models for Zero-Shot Instance Segmentation
Irem Zeynep Alagöz, Nils Morbitzer, Andrea Ramazzina +3
Several disruptive research directions have recently emerged in computer vision, including foundation models achieving previously unseen zero-shot performance in scene understandin…
OR-Action: Multi-Role Video Understanding with Fine-Grained Actions
Felix Tristram, Ege Özsoy, Christian Benz +3
Fine-grained understanding of operating room (OR) activity could enable workflow-aware assistance, yet remains difficult due to clutter, occlusions, and limited sensing. The prevai…
SA4Depth: Consistent Pose-Depth Scale Alignment for Self-Supervised Monocular Depth Estimation
Changxuan Li, Nadine Berner, Nassir Navab +2
Self-supervised depth estimation from monocular sequences relies on the joint learning of a depth and a pose network. Despite abundant research done to improve the depth network, e…
Future Dynamic 3D Reconstruction: Toward 3D World Modeling with Disentangled Ego-Motion
Nils Morbitzer, Jonathan Evers, Artem Savkin +4
Forecasting the evolution of dynamic environments is crucial for autonomous agents. While generative world models have achieved high photorealism in 2D video synthesis by mixing eg…
DisPOSE: Projected Polystochastic Diffusion for Self-Supervised Multi-View 3D Human Pose Estimation
Tony Danjun Wang, Tolga Birdal, Nassir Navab +1
Recovering 3D human poses for multiple individuals from different camera views is a fundamental bottleneck for analyzing interacting behaviors. Existing self-supervised approaches…
DefSynUS: Real-time Patient-specific Intrahepatic Vessel Identification via Deformation-Aware CT-US Domain Adaptation
Karl-Philippe Beaudet, Yordanka Velikova, Sidaty El Hadramy +4
Purpose: Laparoscopic ultrasound (LUS) enhances the safety of liver surgery by visualizing intrahepatic vessels in real-time. Still, vessel identification remains difficult due to…
Look-Closer-Then-Diagnose: Confidence-Aware Ultrasound VQA via Active Zooming
Yue Zhou, Erxuan Wu, Yikang Sun +5
Vision-Language Models (VLMs) have significantly advanced medical visual question answering, yet their performance in ultrasound remains suboptimal. In clinical practice, sonograph…
OpenGaFF: Open-Vocabulary Gaussian Feature Field with Codebook Attention
Kunyi Li, Michael Niemeyer, Sen Wang +3
Understanding open-vocabulary 3D scenes with Gaussian-based representations remains challenging due to fragmented and spatially inconsistent semantic predictions across multi-view…
Millimeter-wave Imaging for Anthropometric Body Measurement
Miriam Senne, Benjamin D. Killeen, Christoph Baur +2
Body shape and circumferences are clinically informative biomarkers for risk stratification, including measures such as waist to hip ratio, limb and trunk girths, yet conventional…
SurgOnAir: Hierarchy-Aware Real-Time Surgical Video Commentary
Jingyi He, Yue Zhou, Long Bai +3
Understanding surgical workflow in real time is fundamental for intelligent surgical embodiment, where AI systems continuously perceive and respond as surgery proceeds. In the oper…
From Adaptation to Generalization: Adaptive Visual Prompting for Medical Image Segmentation
Evren Çetinkaya, Sangmin Lee, Jung Uk Kim +2
Visual prompting has emerged as a powerful method for adapting pre-trained models to new domains without updating model parameters. However, existing prompting methods typically op…
Dyadic Partnership(DP): A Missing Link Towards Full Autonomy in Medical Robotics
Nassir Navab, Zhongliang Jiang
For the past decades medical robotic solutions were mostly based on the concept of tele-manipulation. While their design was extremely intelligent, allowing for better access, impr…
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…
OpenRC: An Open-Source Robotic Colonoscopy Framework for Multimodal Data Acquisition and Autonomy Research
Siddhartha Kapuria, Mohammad Rafiee Javazm, Naruhiko Ikoma +4
Colorectal cancer screening critically depends on colonoscopy, yet existing platforms offer limited support for systematically studying the coupled dynamics of operator control, in…
Non-Rigid 3D Shape Correspondences: From Foundations to Open Challenges and Opportunities
Aleksei Zhuravlev, Lennart Bastian, Dongliang Cao +12
Estimating correspondences between deformed shape instances is a long-standing problem in computer graphics; numerous applications, from texture transfer to statistical modelling,…