Search Results for author:
Found 10 papers, 2 papers with code
A spatio-temporal network for video semantic segmentation in surgical videos
The proposed model includes a spatio-temporal decoder to enable video semantic segmentation by improving temporal consistency across frames.
Self-Knowledge Distillation for Surgical Phase Recognition
Conclusion: We embed a self-knowledge distillation framework for the first time in the surgical phase recognition training pipeline.
ASPnet: Action Segmentation With Shared-Private Representation of Multiple Data Sources
Most state-of-the-art methods for action segmentation are based on single input modalities or naive fusion of multiple data sources.
Ranked #1 on
Action Segmentation
on 50 Salads
(using extra training data)
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
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)
2020 CATARACTS Semantic Segmentation Challenge
In 2020, we released pixel-wise semantic annotations for anatomy and instruments for 4670 images sampled from 25 videos of the CATARACTS training set.
2018 Robotic Scene Segmentation Challenge
In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models.
CaDIS: Cataract Dataset for Image Segmentation
Video feedback provides a wealth of information about surgical procedures and is the main sensory cue for surgeons.
2D Semantic Segmentation task 1 (8 classes)
2D Semantic Segmentation task 2 (17 classes)
+5
SurReal: enhancing Surgical simulation Realism using style transfer
Surgical simulation is an increasingly important element of surgical education.
Hierarchical Piecewise-Constant Super-regions
Recent applications in computer vision have come to heavily rely on superpixel over-segmentation as a pre-processing step for higher level vision tasks, such as object recognition, image labelling or image segmentation.
