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 language | English (US) |
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Title of host publication | Proceedings - 2017 IEEE 30th International Symposium on Computer-Based Medical Systems, CBMS 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 720-725 |
Number of pages | 6 |
Volume | 2017-June |
ISBN (Electronic) | 9781538617106 |
DOIs | |
State | Published - Nov 10 2017 |
Externally published | Yes |
Event | 30th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2017 - Thessaloniki, Greece Duration: Jun 22 2017 → Jun 24 2017 |
Other
Other | 30th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2017 |
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Country | Greece |
City | Thessaloniki |
Period | 6/22/17 → 6/24/17 |
Keywords
- CNN
- Scene Detection
- Similarity Learning
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
- Computer Science Applications