232 citations · 471 across the 17 of their papers we have counts for
17 papers
Mitigating Biases in Surgical Operating Rooms with Geometry
Tony Danjun Wang, Tobias Czempiel, Nassir Navab +1
Deep neural networks are prone to learning spurious correlations, exploiting dataset-specific artifacts rather than meaningful features for prediction. In surgical operating rooms…
Beyond Role-Based Surgical Domain Modeling: Generalizable Re-Identification in the Operating Room
Tony Danjun Wang, Lennart Bastian, Tobias Czempiel +2
Surgical domain models improve workflow optimization through automated predictions of each staff member's surgical role. However, mounting evidence indicates that team familiarity…
DisguisOR: Holistic Face Anonymization for the Operating Room
Lennart Bastian, Tony Danjun Wang, Tobias Czempiel +2
Purpose: Recent advances in Surgical Data Science (SDS) have contributed to an increase in video recordings from hospital environments. While methods such as surgical workflow reco…
Whether and When does Endoscopy Domain Pretraining Make Sense?
Dominik Batić, Felix Holm, Ege Özsoy +2
Automated endoscopy video analysis is a challenging task in medical computer vision, with the primary objective of assisting surgeons during procedures. The difficulty arises from…
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…
Location-Free Scene Graph Generation
Ege Özsoy, Felix Holm, Mahdi Saleh +4
Scene Graph Generation (SGG) is a visual understanding task, aiming to describe a scene as a graph of entities and their relationships with each other. Existing works rely on locat…
CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection
Chinedu Innocent Nwoye, Tong Yu, Saurav Sharma +46
Formalizing surgical activities as triplets of the used instruments, actions performed, and target anatomies is becoming a gold standard approach for surgical activity modeling. Th…
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
Chinedu Innocent Nwoye, Deepak Alapatt, Tong Yu +59
Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works…
4D-OR: Semantic Scene Graphs for OR Domain Modeling
Ege Özsoy, Evin Pınar Örnek, Ulrich Eck +3
Surgical procedures are conducted in highly complex operating rooms (OR), comprising different actors, devices, and interactions. To date, only medically trained human experts are…
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…
Surgical Workflow Recognition: from Analysis of Challenges to Architectural Study
Tobias Czempiel, Aidean Sharghi, Magdalini Paschali +2
Algorithmic surgical workflow recognition is an ongoing research field and can be divided into laparoscopic (Internal) and operating room (External) analysis. So far many different…
Know your sensORs -- A Modality Study For Surgical Action Classification
Lennart Bastian, Tobias Czempiel, Christian Heiliger +4
The surgical operating room (OR) presents many opportunities for automation and optimization. Videos from various sources in the OR are becoming increasingly available. The medical…
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…
OperA: Attention-Regularized Transformers for Surgical Phase Recognition
Tobias Czempiel, Magdalini Paschali, Daniel Ostler +3
In this paper we introduce OperA, a transformer-based model that accurately predicts surgical phases from long video sequences. A novel attention regularization loss encourages the…
Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs
Seong Tae Kim, Leili Goli, Magdalini Paschali +7
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 a…
TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks
Tobias Czempiel, Magdalini Paschali, Matthias Keicher +4
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…