Search Results for author:
Found 10 papers, 7 papers with code
PitVis-2023 Challenge: Workflow Recognition in videos of Endoscopic Pituitary Surgery
The field of computer vision applied to videos of minimally invasive surgery is ever-growing.
SurgicaL-CD: Generating Surgical Images via Unpaired Image Translation with Latent Consistency Diffusion Models
Computer-assisted surgery (CAS) systems are designed to assist surgeons during procedures, thereby reducing complications and enhancing patient care.
Exploring Semantic Consistency in Unpaired Image Translation to Generate Data for Surgical Applications
This study empirically investigates unpaired image translation methods for generating suitable data in surgical applications, explicitly focusing on semantic consistency.
TUNeS: A Temporal U-Net with Self-Attention for Video-based Surgical Phase Recognition
In addition, we propose to train the feature extractor, a standard CNN, together with an LSTM on preferably long video segments, i. e., with long temporal context.
Metrics Matter in Surgical Phase Recognition
Surgical phase recognition is a basic component for different context-aware applications in computer- and robot-assisted surgery.
On the Pitfalls of Batch Normalization for End-to-End Video Learning: A Study on Surgical Workflow Analysis
In this paper, we analyze pitfalls of BN in video learning, including issues specific to online tasks such as a 'cheating' effect in anticipation.
Long-Term Temporally Consistent Unpaired Video Translation from Simulated Surgical 3D Data
However for transfer from simulated to photorealistic sequences, available information on the underlying geometry offers potential for achieving global consistency across views.
Rethinking Anticipation Tasks: Uncertainty-aware Anticipation of Sparse Surgical Instrument Usage for Context-aware Assistance
We propose a novel learning task for anticipation of instrument usage in laparoscopic videos that overcomes these limitations.
Unsupervised Temporal Video Segmentation as an Auxiliary Task for Predicting the Remaining Surgery Duration
Using our unsupervised method as an auxiliary task for RSD training, we outperform other self-supervised methods and are comparable to the supervised state-of-the-art.
Active Learning using Deep Bayesian Networks for Surgical Workflow Analysis
For many applications in the field of computer assisted surgery, such as providing the position of a tumor, specifying the most probable tool required next by the surgeon or determining the remaining duration of surgery, methods for surgical workflow analysis are a prerequisite.
