Surgical tool detection
9 papers with code • 2 benchmarks • 5 datasets
Presence detection of various classes of surgical instruments in endoscopy videos.
Benchmarks
These leaderboards are used to track progress in Surgical tool detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
Cholec80
CholecT50
HeiChole Benchmark
PWISeg
CholecTrack20
Latest papers
PWISeg: Point-based Weakly-supervised Instance Segmentation for Surgical Instruments
To address this issue, we propose a novel yet effective weakly-supervised surgical instrument instance segmentation approach, named Point-based Weakly-supervised Instance Segmentation (PWISeg).
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.
A semi-supervised Teacher-Student framework for surgical tool detection and localization
mansoor-at/semi-supervised-surgical-tool-detection
•
•
Therefore, in this paper we introduce a semi-supervised learning (SSL) framework in surgical tool detection paradigm which aims to mitigate the scarcity of training data and the data imbalance through a knowledge distillation approach.
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%.
Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis
Mutually leveraging both low-level feature sharing and high-level prediction correlating, our MTRCNet-CL method can encourage the interactions between the two tasks to a large extent, and hence can bring about benefits to each other.
Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic Videos
CAMMA-public/ConvLSTM-Surgical-Tool-Tracker
•
•
Results: We build a baseline tracker on top of the CNN model and demonstrate that our approach based on the ConvLSTM outperforms the baseline in tool presence detection, spatial localization, and motion tracking by over 5. 0%, 13. 9%, and 12. 6%, respectively.
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.
EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos
In the literature, two types of features are typically used to perform this task: visual features and tool usage signals.
SSD: Single Shot MultiBox Detector
Experimental results on the PASCAL VOC, MS COCO, and ILSVRC datasets confirm that SSD has comparable accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference.
