11 citations · 14 across the 12 of their papers we have counts for
16 papers
Calibrated Confidence Expression for Radiology Report Generation
David Bani-Harouni, Chantal Pellegrini, Julian Lüers +6
Safe deployment of Large Vision-Language Models (LVLMs) in radiology report generation requires not only accurate predictions but also clinically interpretable indicators of when o…
Prototype-Based Knowledge Guidance for Fine-Grained Structured Radiology Reporting
Chantal Pellegrini, Adrian Delchev, Ege Özsoy +2
Structured radiology reporting promises faster, more consistent communication than free text, but automation remains difficult as models must make many fine-grained, discrete decis…
SpeechCT-CLIP: Distilling Text-Image Knowledge to Speech for Voice-Native Multimodal CT Analysis
Lukas Buess, Jan Geier, David Bani-Harouni +6
Spoken communication plays a central role in clinical workflows. In radiology, for example, most reports are created through dictation. Yet, nearly all medical AI systems rely excl…
Language Agents for Hypothesis-driven Clinical Decision Making with Reinforcement Learning
David Bani-Harouni, Chantal Pellegrini, Ege Özsoy +2
Clinical decision-making is a dynamic, interactive, and cyclic process where doctors have to repeatedly decide on which clinical action to perform and consider newly uncovered info…
EHR2Path: Comprehensive Pathway-Level Modeling of Longitudinal Patient Trajectories from Multimodal Electronic Health Records
Chantal Pellegrini, Ege Özsoy, David Bani-Harouni +2
Forecasting how a patient's condition is likely to evolve, including possible deterioration, recovery, treatment needs, and care transitions, could support more proactive and perso…
EgoExOR: An Ego-Exo-Centric Operating Room Dataset for Surgical Activity Understanding
Ege Özsoy, Arda Mamur, Felix Tristram +4
Operating rooms (ORs) demand precise coordination among surgeons, nurses, and equipment in a fast-paced, occlusion-heavy environment, necessitating advanced perception models to en…
Specialized Foundation Models for Intelligent Operating Rooms
Ege Özsoy, Chantal Pellegrini, David Bani-Harouni +3
Surgical procedures unfold in complex environments demanding coordination between surgical teams, tools, imaging and increasingly, intelligent robotic systems. Ensuring safety and…
MM-OR: A Large Multimodal Operating Room Dataset for Semantic Understanding of High-Intensity Surgical Environments
Ege Özsoy, Chantal Pellegrini, Tobias Czempiel +7
Operating rooms (ORs) are complex, high-stakes environments requiring precise understanding of interactions among medical staff, tools, and equipment for enhancing surgical assista…
Rewarding Doubt: A Reinforcement Learning Approach to Calibrated Confidence Expression of Large Language Models
David Bani-Harouni, Chantal Pellegrini, Paul Stangel +4
A safe and trustworthy use of Large Language Models (LLMs) requires an accurate expression of confidence in their answers. We propose a novel Reinforcement Learning approach that a…
ORacle: Large Vision-Language Models for Knowledge-Guided Holistic OR Domain Modeling
Ege Özsoy, Chantal Pellegrini, Matthias Keicher +1
Every day, countless surgeries are performed worldwide, each within the distinct settings of operating rooms (ORs) that vary not only in their setups but also in the personnel, too…
RaDialog: A Large Vision-Language Model for Radiology Report Generation and Conversational Assistance
Chantal Pellegrini, Ege Özsoy, Benjamin Busam +2
Conversational AI tools that can generate and discuss clinically correct radiology reports for a given medical image have the potential to transform radiology. Such a human-in-the-…
Rad-ReStruct: A Novel VQA Benchmark and Method for Structured Radiology Reporting
Chantal Pellegrini, Matthias Keicher, Ege Özsoy +1
Radiology reporting is a crucial part of the communication between radiologists and other medical professionals, but it can be time-consuming and error-prone. One approach to allev…
LABRAD-OR: Lightweight Memory Scene Graphs for Accurate Bimodal Reasoning in Dynamic Operating Rooms
Ege Özsoy, Tobias Czempiel, Felix Holm +2
Modern surgeries are performed in complex and dynamic settings, including ever-changing interactions between medical staff, patients, and equipment. The holistic modeling of the op…
Xplainer: From X-Ray Observations to Explainable Zero-Shot Diagnosis
Chantal Pellegrini, Matthias Keicher, Ege Özsoy +3
Automated diagnosis prediction from medical images is a valuable resource to support clinical decision-making. However, such systems usually need to be trained on large amounts of…
Unsupervised pre-training of graph transformers on patient population graphs
Chantal Pellegrini, Nassir Navab, Anees Kazi
Pre-training has shown success in different areas of machine learning, such as Computer Vision, Natural Language Processing (NLP), and medical imaging. However, it has not been ful…
Unsupervised Pre-Training on Patient Population Graphs for Patient-Level Predictions
Chantal Pellegrini, Anees Kazi, Nassir Navab
Pre-training has shown success in different areas of machine learning, such as Computer Vision (CV), Natural Language Processing (NLP) and medical imaging. However, it has not been…