Search Results for author:
Found 16 papers, 7 papers with code
TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks
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.
Ranked #4 on
Surgical phase recognition
on Cholec80
4D-OR: Semantic Scene Graphs for OR Domain Modeling
Towards this goal, for the first time, we propose using semantic scene graphs (SSG) to describe and summarize the surgical scene.
Ranked #4 on
Scene Graph Generation
on 4D-OR
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge.
Ranked #1 on
Action Triplet Recognition
on CholecT50 (Challenge)
(using extra training data)
CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection
This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection.
Ranked #1 on
Action Triplet Detection
on CholecT50 (Challenge)
LABRAD-OR: Lightweight Memory Scene Graphs for Accurate Bimodal Reasoning in Dynamic Operating Rooms
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.
Ranked #3 on
Scene Graph Generation
on 4D-OR
Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs
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.
DisguisOR: Holistic Face Anonymization for the Operating Room
Methods: RGB and depth images from multiple cameras are fused into a 3D point cloud representation of the scene.
OperA: Attention-Regularized Transformers for Surgical Phase Recognition
In this paper we introduce OperA, a transformer-based model that accurately predicts surgical phases from long video sequences.
U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction
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.
Know your sensORs -- A Modality Study For Surgical Action Classification
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.
Surgical Workflow Recognition: from Analysis of Challenges to Architectural Study
Algorithmic surgical workflow recognition is an ongoing research field and can be divided into laparoscopic (Internal) and operating room (External) analysis.
Longitudinal Self-Supervision for COVID-19 Pathology Quantification
For this purpose, longitudinal self-supervision schemes are explored on clinical longitudinal COVID-19 CT scans.
FlexR: Few-shot Classification with Language Embeddings for Structured Reporting of Chest X-rays
The automation of chest X-ray reporting has garnered significant interest due to the time-consuming nature of the task.
Location-Free Scene Graph Generation
Although using significantly fewer labels during training, we achieve 74. 12\% of the location-supervised SOTA performance on Visual Genome and even outperform the best method on 4D-OR.
Ranked #2 on
Scene Graph Generation
on 4D-OR
Whether and When does Endoscopy Domain Pretraining Make Sense?
In this work, we investigate the need for endoscopy domain-specific pretraining based on downstream objectives.
Dynamic Scene Graph Representation for Surgical Video
Surgical videos captured from microscopic or endoscopic imaging devices are rich but complex sources of information, depicting different tools and anatomical structures utilized during an extended amount of time.
