WeChat Mini Program
Old Version Features

    • Semi-supervised Surgical Tool Detection Based on Highly Confident Pseudo Labeling and Strong Augmentation Driven Consistency

    Wenjing Jiang,Tong Xia,Zhiqiong Wang,Fucang Jia

    semanticscholar

    College of Medicine and Biomedical Information Engineering

    Cited 5|Views6
    Abstract
    Surgical tool detection in computer-assisted intervention system aims to provide surgeons with specific supportive information. Existing supervised methods heavily rely on the volume of labeled data. However, manually annotating location of tools in surgical videos is quite time-consuming. To overcome this problem, we propose a semi-supervised pipeline for surgical tool detection, using strategies of highly confident pseudo labeling and strong augmentation driven consistency. To evaluate the proposed pipeline, we introduce a surgical tool detection dataset, Cataract Dataset for Tool Detection (CaDTD). Compared to the supervised baseline, our semi-supervised method improves mean average precision (mAP) by 4.3%. In addition, an ablative study was conducted to validate the effectiveness of the two strategies in our tool detection pipeline, and the results show the mAP improvement of 1.9% and 3.9%, respectively. The proposed dataset, CaDTD, is publicly available at https://github.com/evangel-jiang/CaDTD.
    More
    Translated text
    Key words
    Surgical video analysis,Tool detection,Semi-supervised learning,Pseudo labeling,Strong augmentation
    上传PDF
    View via Publisher
    Bibtex
    收藏
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined