Real-Time Instrument Scene Detection in Screening GI Endoscopic Procedures

Chuanhai Zhang, Wallapak Tavanapong, Johnny Wong, Piet C.De Groen, Junghwan Oh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We describe a new and effective real-time solution for detecting video segments showing an instrument used during diagnostic or therapeutic operations in endoscopic procedures. In addition, we present a new method to collect a large training dataset: similarity-based data augmentation. This method automates most of the creation of a large training dataset and prevents extensive manual effort to collect and annotate training data by domain experts. Convolutional Neural Network (CNN) analysis using the training data collected with similarity-based data augmentation yields an average F1 score within 1% of that of the CNN analysis using a large manually collected training dataset.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE 30th International Symposium on Computer-Based Medical Systems, CBMS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages720-725
Number of pages6
Volume2017-June
ISBN (Electronic)9781538617106
DOIs
StatePublished - Nov 10 2017
Externally publishedYes
Event30th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2017 - Thessaloniki, Greece
Duration: Jun 22 2017Jun 24 2017

Other

Other30th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2017
CountryGreece
CityThessaloniki
Period6/22/176/24/17

Keywords

  • CNN
  • Scene Detection
  • Similarity Learning

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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  • Cite this

    Zhang, C., Tavanapong, W., Wong, J., Groen, P. C. D., & Oh, J. (2017). Real-Time Instrument Scene Detection in Screening GI Endoscopic Procedures. In Proceedings - 2017 IEEE 30th International Symposium on Computer-Based Medical Systems, CBMS 2017 (Vol. 2017-June, pp. 720-725). [8104286] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CBMS.2017.42