Addressing multi-label imbalance problem of Surgical Tool Detection using CNN
- Purpose: A fully automated surgical tool detection framework is proposed for endoscopic video streams. State-of-the-art surgical tool detection methods rely on supervised one-vs-all or multi-class classification techniques, completely ignoring the co-occurrence relationship of the tools and the associated class imbalance. Methods: In this paper, we formulate tool detection as a multi-label classification task where tool co-occurrences are treated as separate classes. In addition, imbalance on tool co-occurrences is analyzed and stratification techniques are employed to address the imbalance during Convolutional Neural Network (CNN) training. Moreover, temporal smoothing is introduced as an online post-processing step to enhance run time prediction. Results: Quantitative analysis is performed on the M2CAI16 tool detection dataset to highlight the importance of stratification, temporal smoothing and the overall framework for tool detection. Conclusion: The analysis on tool imbalance, backed by the empirical results indicates the need and superiority of the proposed framework over state-of-the-art techniques.
Author: | Manish Sahu, Anirban Mukhopadhyay, Angelika Szengel, Stefan ZachowORCiD |
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Document Type: | Article |
Parent Title (English): | International Journal of Computer Assisted Radiology and Surgery |
Volume: | 12 |
Issue: | 6 |
First Page: | 1013 |
Last Page: | 1020 |
Publisher: | Springer |
Year of first publication: | 2017 |
Page Number: | 8 |
Notes: | Selected for final oral presentation |
URL: | https://link.springer.com/article/10.1007/s11548-017-1565-x |
DOI: | https://doi.org/10.1007/s11548-017-1565-x |