Search Results for author: Tobias Czempiel

Found 10 papers, 3 papers with code

Few-shot Structured Radiology Report Generation Using Natural Language Prompts

no code implementations29 Mar 2022 Matthias Keicher, Kamilia Mullakaeva, Tobias Czempiel, Kristina Mach, Ashkan Khakzar, Nassir Navab

Chest radiograph reporting is time-consuming, and numerous solutions to automate this process have been proposed.

4D-OR: Semantic Scene Graphs for OR Domain Modeling

no code implementations22 Mar 2022 Ege Özsoy, Evin Pınar Örnek, Ulrich Eck, Tobias Czempiel, Federico Tombari, Nassir Navab

Towards this goal, for the first time, we propose using semantic scene graphs (SSG) to describe and summarize the surgical scene.

Scene Graph Generation

Surgical Workflow Recognition: from Analysis of Challenges to Architectural Study

no code implementations17 Mar 2022 Tobias Czempiel, Aidean Sharghi, Magdalini Paschali, Omid Mohareri

Algorithmic surgical workflow recognition is an ongoing research field and can be divided into laparoscopic (Internal) and operating room (External) analysis.

Know your sensORs -- A Modality Study For Surgical Action Classification

no code implementations16 Mar 2022 Lennart Bastian, Tobias Czempiel, Christian Heiliger, Konrad Karcz, Ulrich Eck, Benjamin Busam, Nassir Navab

Existing datasets from OR room cameras are thus far limited in size or modalities acquired, leaving it unclear which sensor modalities are best suited for tasks such as recognizing surgical action from videos.

Action Classification Action Recognition +1

U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction

no code implementations29 Jul 2021 Matthias Keicher, Hendrik Burwinkel, David Bani-Harouni, Magdalini Paschali, Tobias Czempiel, Egon Burian, Marcus R. Makowski, Rickmer Braren, Nassir Navab, Thomas Wendler

Specifically, we introduce a multimodal similarity metric to build a population graph for clustering patients and an image-based end-to-end Graph Attention Network to process this graph and predict the COVID-19 patient outcomes: admission to ICU, need for ventilation and mortality.

Decision Making Graph Attention

Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs

1 code implementation12 Mar 2021 Seong Tae Kim, Leili Goli, Magdalini Paschali, Ashkan Khakzar, Matthias Keicher, Tobias Czempiel, Egon Burian, Rickmer Braren, Nassir Navab, Thomas Wendler

Chest computed tomography (CT) has played an essential diagnostic role in assessing patients with COVID-19 by showing disease-specific image features such as ground-glass opacity and consolidation.

Computed Tomography (CT) COVID-19 Image Segmentation +2

OperA: Attention-Regularized Transformers for Surgical Phase Recognition

no code implementations5 Mar 2021 Tobias Czempiel, Magdalini Paschali, Daniel Ostler, Seong Tae Kim, Benjamin Busam, Nassir Navab

In this paper we introduce OperA, a transformer-based model that accurately predicts surgical phases from long video sequences.

TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks

2 code implementations24 Mar 2020 Tobias Czempiel, Magdalini Paschali, Matthias Keicher, Walter Simson, Hubertus Feussner, Seong Tae Kim, Nassir Navab

Automatic surgical phase recognition is a challenging and crucial task with the potential to improve patient safety and become an integral part of intra-operative decision-support systems.

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