Search Results for author:
Found 12 papers, 7 papers with code
The Endoscapes Dataset for Surgical Scene Segmentation, Object Detection, and Critical View of Safety Assessment: Official Splits and Benchmark
This technical report provides a detailed overview of Endoscapes, a dataset of laparoscopic cholecystectomy (LC) videos with highly intricate annotations targeted at automated assessment of the Critical View of Safety (CVS).
Encoding Surgical Videos as Latent Spatiotemporal Graphs for Object and Anatomy-Driven Reasoning
Recently, spatiotemporal graphs have emerged as a concise and elegant manner of representing video clips in an object-centric fashion, and have shown to be useful for downstream tasks such as action recognition.
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)
Preserving Privacy in Surgical Video Analysis Using Artificial Intelligence: A Deep Learning Classifier to Identify Out-of-Body Scenes in Endoscopic Videos
Results: The internal dataset consisting of 356, 267 images from 48 videos and the two multicentric test datasets consisting of 54, 385 and 58, 349 images from 10 and 20 videos, respectively, were annotated.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Real-Time Artificial Intelligence Assistance for Safe Laparoscopic Cholecystectomy: Early-Stage Clinical Evaluation
Artificial intelligence is set to be deployed in operating rooms to improve surgical care.
Latent Graph Representations for Critical View of Safety Assessment
Assessing the critical view of safety in laparoscopic cholecystectomy requires accurate identification and localization of key anatomical structures, reasoning about their geometric relationships to one another, and determining the quality of their exposure.
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)
Temporally Constrained Neural Networks (TCNN): A framework for semi-supervised video semantic segmentation
A major obstacle to building models for effective semantic segmentation, and particularly video semantic segmentation, is a lack of large and well annotated datasets.
Ranked #2 on
Semantic Segmentation
on Endoscapes
Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark
PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems.
Ranked #2 on
Surgical phase recognition
on HeiChole Benchmark
Surgical data science for safe cholecystectomy: a protocol for segmentation of hepatocystic anatomy and assessment of the critical view of safety
Minimally invasive image-guided surgery heavily relies on vision.
Weakly-Supervised Learning for Tool Localization in Laparoscopic Videos
We propose a deep architecture, trained solely on image level annotations, that can be used for both tool presence detection and localization in surgical videos.
Ranked #4 on
Surgical tool detection
on Cholec80
