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Found 9 papers, 7 papers with code
Optimizing Latent Graph Representations of Surgical Scenes for Zero-Shot Domain Transfer
Purpose: Advances in deep learning have resulted in effective models for surgical video analysis; however, these models often fail to generalize across medical centers due to domain shift caused by variations in surgical workflow, camera setups, and patient demographics.
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.
Jumpstarting Surgical Computer Vision
Methods: In this work, we employ self-supervised learning to flexibly leverage diverse surgical datasets, thereby learning taskagnostic representations that can be used for various surgical downstream tasks.
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)
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.
Dissecting Self-Supervised Learning Methods for Surgical Computer Vision
Correct transfer of these methods to surgery, as described and conducted in this work, leads to substantial performance gains over generic uses of SSL - up to 7. 4% on phase recognition and 20% on tool presence detection - as well as state-of-the-art semi-supervised phase recognition approaches by up to 14%.
Ranked #1 on
Semantic Segmentation
on Endoscapes
"Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task Transfer
Based upon this insight, we propose See-SPOT-Run (SSR), a new computational approach to robot learning that enables a robot to complete a variety of real robot tasks in novel problem domains without task-specific training.
Guiding Multi-Step Rearrangement Tasks with Natural Language Instructions
Our model completes block manipulation tasks with synthetic commands 530 more often than a UNet-based baseline, and learns to localize actions correctly while creating a mapping of symbols to perceptual input that supports compositional reasoning.
