Search Results for author: Chantal Pellegrini

Found 7 papers, 6 papers with code

ORacle: Large Vision-Language Models for Knowledge-Guided Holistic OR Domain Modeling

no code implementations10 Apr 2024 Ege Özsoy, Chantal Pellegrini, Matthias Keicher, Nassir Navab

This demonstrates ORacle's potential to significantly enhance the scalability and affordability of OR domain modeling and opens a pathway for future advancements in surgical data science.

Data Augmentation Graph Generation +2

RaDialog: A Large Vision-Language Model for Radiology Report Generation and Conversational Assistance

1 code implementation30 Nov 2023 Chantal Pellegrini, Ege Özsoy, Benjamin Busam, Nassir Navab, Matthias Keicher

Conversational AI tools that can generate and discuss clinically correct radiology reports for a given medical image have the potential to transform radiology.

Language Modelling Large Language Model

Rad-ReStruct: A Novel VQA Benchmark and Method for Structured Radiology Reporting

1 code implementation11 Jul 2023 Chantal Pellegrini, Matthias Keicher, Ege Özsoy, Nassir Navab

However, there is limited research on automating structured reporting, and no public benchmark is available for evaluating and comparing different methods.

Medical Visual Question Answering Question Answering +2

LABRAD-OR: Lightweight Memory Scene Graphs for Accurate Bimodal Reasoning in Dynamic Operating Rooms

1 code implementation23 Mar 2023 Ege Özsoy, Tobias Czempiel, Felix Holm, Chantal Pellegrini, Nassir Navab

The holistic representation of surgical scenes as semantic scene graphs (SGG), where entities are represented as nodes and relations between them as edges, is a promising direction for fine-grained semantic OR understanding.

Scene Graph Generation

Unsupervised pre-training of graph transformers on patient population graphs

2 code implementations21 Jul 2022 Chantal Pellegrini, Nassir Navab, Anees Kazi

We find that our proposed pre-training methods help in modeling the data at a patient and population level and improve performance in different fine-tuning tasks on all datasets.

Language Modelling Masked Language Modeling +2

Unsupervised Pre-Training on Patient Population Graphs for Patient-Level Predictions

2 code implementations23 Mar 2022 Chantal Pellegrini, Anees Kazi, Nassir Navab

We test our method on two medical datasets of patient records, TADPOLE and MIMIC-III, including imaging and non-imaging features and different prediction tasks.

Disease Prediction Imputation +2

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