Addressing multi-label imbalance problem of surgical tool detection using CNN
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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.
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 runtime prediction.
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.
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.
KeywordsTransfer learning Surgical tool detection CNN Laparoscopic videos Multi-label learning
This study was funded by German Federal Ministry of Education and Research (BMBF) under the project BIOPASS (Grant No. 16 5V 7257).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
Human and animal rights statement
This article does not contain any studies with human participants or animals performed by any of the authors.
This article contains patient data from a publically available dataset.
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