232 citations · 328 across the 25 of their papers we have counts for
13 papers · 1 filter
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…
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…
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…
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…
GLOWin: A Flow-based Invertible Generative Framework for Learning Disentangled Feature Representations in Medical Images
Aadhithya Sankar, Matthias Keicher, Rami Eisawy +4
Disentangled representations can be useful in many downstream tasks, help to make deep learning models more interpretable, and allow for control over features of synthetically gene…
Continual Class Incremental Learning for CT Thoracic Segmentation
Abdelrahman Elskhawy, Aneta Lisowska, Matthias Keicher +3
Deep learning organ segmentation approaches require large amounts of annotated training data, which is limited in supply due to reasons of confidentiality and the time required for…