2 citations · 4 across the 20 of their papers we have counts for
21 papers
HeiCo-FOCUS: A Clinically Grounded Dataset for Long-Context Video Understanding
Leon Mayer, Lucas Luttner, Patrick Godau +42
Recent advances in Vision-Language Models (VLMs) have led to rapid progress in video understanding across a wide range of benchmark tasks. However, existing evaluations largely foc…
Looking Beyond the Scale: Do Surgical Skill Models Learn Transferable Representations Across Assessment Rubrics?
Hanna Hoffmann, Felix von Bechtolsheim, Stefanie Speidel +1
Vision-based surgical skill assessment has shown strong in-domain results, yet a fundamental question remains unasked: do these models learn transferable representations of surgica…
Understanding Multimodality in Generative Behavioral Cloning
Lorenzo Mazza, Massimiliano Datres, Ariel Rodriguez +3
Behavioral cloning becomes challenging when the same observation admits several valid actions. We study how generative behavioral-cloning policies represent such multimodal expert…
OSS: Open Suturing Skills Vision-Based Assessment Challenge 2024-2025
Hanna Hoffmann, Setareh Bady, Claas de Boer +54
Achieving high levels of surgical skill through effective training is essential for optimal patient outcomes. Automated, data-driven skill assessment holds significant potential to…
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…
An Open-Source Robotics Research Platform for Autonomous Laparoscopic Surgery
Ariel Rodriguez, Lorenzo Mazza, Martin Lelis +4
Autonomous robot-assisted surgery demands reliable, high-precision platforms that strictly adhere to the safety and kinematic constraints of minimally invasive procedures. Existing…
LAR-MoE: Latent-Aligned Routing for Mixture of Experts in Robotic Imitation Learning
Ariel Rodriguez, Chenpan Li, Lorenzo Mazza +5
Imitation learning enables robots to acquire manipulation skills from demonstrations, yet deploying a policy across tasks with heterogeneous dynamics remains challenging, as models…
The Dresden Dataset for 4D Reconstruction of Non-Rigid Abdominal Surgical Scenes
Reuben Docea, Rayan Younis, Yonghao Long +10
The D4D Dataset provides paired endoscopic video and high-quality structured-light geometry for evaluating 3D reconstruction of deforming abdominal soft tissue in realistic surgica…
A benchmark for video-based laparoscopic skill analysis and assessment
Isabel Funke, Sebastian Bodenstedt, Felix von Bechtolsheim +7
Laparoscopic surgery is a complex surgical technique that requires extensive training. Recent advances in deep learning have shown promise in supporting this training by enabling a…
Supervised Mixture-of-Experts for Surgical Grasping and Retraction
Lorenzo Mazza, Ariel Rodriguez, Rayan Younis +6
Imitation learning has achieved remarkable success in robotic manipulation, yet its application to surgical robotics remains challenging due to data scarcity, constrained workspace…
XiCAD: Camera Activation Detection in the Da Vinci Xi User Interface
Alexander C. Jenke, Gregor Just, Claas de Boer +3
Purpose: Robot-assisted minimally invasive surgery relies on endoscopic video as the sole intraoperative visual feedback. The DaVinci Xi system overlays a graphical user interface…
Three-Dimensional Anatomical Data Generation Based on Artificial Neural Networks
Ann-Sophia Müller, Moonkwang Jeong, Meng Zhang +4
Surgical planning and training based on machine learning requires a large amount of 3D anatomical models reconstructed from medical imaging, which is currently one of the major bot…
Current validation practice undermines surgical AI development
Annika Reinke, Ziying O. Li, Minu D. Tizabi +98
Surgical data science (SDS) is rapidly advancing, yet clinical adoption of artificial intelligence (AI) in surgery remains limited, with inadequate validation as an important contr…
Towards Application Aligned Synthetic Surgical Image Synthesis
Danush Kumar Venkatesh, Stefanie Speidel
The scarcity of annotated surgical data poses a significant challenge for developing deep learning systems in computer-assisted interventions. While diffusion models can synthesize…
PIVOTS: Aligning unseen Structures using Preoperative to Intraoperative Volume-To-Surface Registration for Liver Navigation
Peng Liu, Bianca Güttner, Yutong Su +16
Non-rigid registration is essential for Augmented Reality guided laparoscopic liver surgery by fusing preoperative information, such as tumor location and vascular structures, into…
Mission Balance: Generating Under-represented Class Samples using Video Diffusion Models
Danush Kumar Venkatesh, Isabel Funke, Micha Pfeiffer +5
Computer-assisted interventions can improve intra-operative guidance, particularly through deep learning methods that harness the spatiotemporal information in surgical videos. How…
Federated EndoViT: Pretraining Vision Transformers via Federated Learning on Endoscopic Image Collections
Max Kirchner, Alexander C. Jenke, Sebastian Bodenstedt +5
Purpose: Data privacy regulations hinder the creation of generalizable foundation models (FMs) for surgery by preventing multi-institutional data aggregation. This study investigat…
Data Augmentation for Surgical Scene Segmentation with Anatomy-Aware Diffusion Models
Danush Kumar Venkatesh, Dominik Rivoir, Micha Pfeiffer +2
In computer-assisted surgery, automatically recognizing anatomical organs is crucial for understanding the surgical scene and providing intraoperative assistance. While machine lea…
SurgicaL-CD: Generating Surgical Images via Unpaired Image Translation with Latent Consistency Diffusion Models
Danush Kumar Venkatesh, Dominik Rivoir, Micha Pfeiffer +1
Computer-assisted surgery (CAS) systems are designed to assist surgeons during procedures, thereby reducing complications and enhancing patient care. Training machine learning mode…
One model to use them all: Training a segmentation model with complementary datasets
Alexander C. Jenke, Sebastian Bodenstedt, Fiona R. Kolbinger +3
Understanding a surgical scene is crucial for computer-assisted surgery systems to provide any intelligent assistance functionality. One way of achieving this scene understanding i…
Graph data modelling for outcome prediction in oropharyngeal cancer patients
Nithya Bhasker, Stefan Leger, Alexander Zwanenburg +4
Graph neural networks (GNNs) are becoming increasingly popular in the medical domain for the tasks of disease classification and outcome prediction. Since patient data is not readi…