232 citations · 328 across the 25 of their papers we have counts for
29 papers
Learning Diagnostic Reasoning for Decision Support in Toxicology
Nico Oberländer, David Bani-Harouni, Tobias Zellner +3
Acute poly-substance intoxication requires rapid, life-saving decisions under substantial uncertainty, as clinicians must rely on incomplete ingestion details and nonspecific sympt…
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…
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…
From large language models to multimodal AI: A scoping review on the potential of generative AI in medicine
Lukas Buess, Matthias Keicher, Nassir Navab +2
Generative artificial intelligence (AI) models, such as diffusion models and OpenAI's ChatGPT, are transforming medicine by enhancing diagnostic accuracy and automating clinical wo…
Counterfactual Explanations for Medical Image Classification and Regression using Diffusion Autoencoder
Matan Atad, David Schinz, Hendrik Moeller +6
Counterfactual explanations (CEs) aim to enhance the interpretability of machine learning models by illustrating how alterations in input features would affect the resulting predic…
MAGDA: Multi-agent guideline-driven diagnostic assistance
David Bani-Harouni, Nassir Navab, Matthias Keicher
In emergency departments, rural hospitals, or clinics in less developed regions, clinicians often lack fast image analysis by trained radiologists, which can have a detrimental eff…
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…
Semantic Latent Space Regression of Diffusion Autoencoders for Vertebral Fracture Grading
Matthias Keicher, Matan Atad, David Schinz +9
Vertebral fractures are a consequence of osteoporosis, with significant health implications for affected patients. Unfortunately, grading their severity using CT exams is hard and…
Prior-RadGraphFormer: A Prior-Knowledge-Enhanced Transformer for Generating Radiology Graphs from X-Rays
Yiheng Xiong, Jingsong Liu, Kamilia Zaripova +3
The extraction of structured clinical information from free-text radiology reports in the form of radiology graphs has been demonstrated to be a valuable approach for evaluating th…
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…
CheXplaining in Style: Counterfactual Explanations for Chest X-rays using StyleGAN
Matan Atad, Vitalii Dmytrenko, Yitong Li +6
Deep learning models used in medical image analysis are prone to raising reliability concerns due to their black-box nature. To shed light on these black-box models, previous works…
U-PET: MRI-based Dementia Detection with Joint Generation of Synthetic FDG-PET Images
Marcel Kollovieh, Matthias Keicher, Stephan Wunderlich +3
Alzheimer's disease (AD) is the most common cause of dementia. An early detection is crucial for slowing down the disease and mitigating risks related to the progression. While the…
Interpretable Vertebral Fracture Diagnosis
Paul Engstler, Matthias Keicher, David Schinz +11
Do black-box neural network models learn clinically relevant features for fracture diagnosis? The answer not only establishes reliability quenches scientific curiosity but also lea…
Longitudinal Self-Supervision for COVID-19 Pathology Quantification
Tobias Czempiel, Coco Rogers, Matthias Keicher +7
Quantifying COVID-19 infection over time is an important task to manage the hospitalization of patients during a global pandemic. Recently, deep learning-based approaches have been…
FlexR: Few-shot Classification with Language Embeddings for Structured Reporting of Chest X-rays
Matthias Keicher, Kamilia Zaripova, Tobias Czempiel +3
The automation of chest X-ray reporting has garnered significant interest due to the time-consuming nature of the task. However, the clinical accuracy of free-text reports has prov…
U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction
Matthias Keicher, Hendrik Burwinkel, David Bani-Harouni +7
During the first wave of COVID-19, hospitals were overwhelmed with the high number of admitted patients. An accurate prediction of the most likely individual disease progression ca…