Surgical Phase and Instrument Recognition: How to identify appropriate Dataset Splits
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866929233525211136 |
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| author | Kostiuchik, Georgii Sharan, Lalith Mayer, Benedikt Wolf, Ivo Preim, Bernhard Engelhardt, Sandy |
| author_facet | Kostiuchik, Georgii Sharan, Lalith Mayer, Benedikt Wolf, Ivo Preim, Bernhard Engelhardt, Sandy |
| contents | Purpose: Machine learning models can only be reliably evaluated if training, validation, and test data splits are representative and not affected by the absence of classes of interest. Surgical workflow and instrument recognition tasks are complicated in this manner, because of heavy data imbalances resulting from different lengths of phases and their erratic occurrences. Furthermore, the issue becomes difficult as sub-properties that help define phases, like instrument (co-)occurrence, are usually not considered when defining the split. We argue that such sub-properties must be equally considered.
Methods: This work presents a publicly available data visualization tool that enables interactive exploration of dataset splits for surgical phase and instrument recognition. It focuses on the visualization of the occurrence of phases, phase transitions, instruments, and instrument combinations across sets. Particularly, it facilitates the assessment and identification of sub-optimal dataset splits.
Results: We performed an analysis of common Cholec80 dataset splits using the proposed application and were able to uncover phase transitions and combinations of instruments that were not represented in one of the sets. Additionally, we outlined possible improvements to the splits. A user study with ten participants demonstrated the ability of participants to solve a selection of data exploration tasks using the proposed application.
Conclusion: In highly unbalanced class distributions, special care should be taken with respect to the selection of an appropriate dataset split. Our interactive data visualization tool presents a promising approach for the assessment of dataset splits for surgical phase and instrument recognition. Evaluation results show that it can enhance the development of machine learning models. The application is available at https://cardio-ai.github.io/endovis-ml/ . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_16879 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Surgical Phase and Instrument Recognition: How to identify appropriate Dataset Splits Kostiuchik, Georgii Sharan, Lalith Mayer, Benedikt Wolf, Ivo Preim, Bernhard Engelhardt, Sandy Machine Learning Purpose: Machine learning models can only be reliably evaluated if training, validation, and test data splits are representative and not affected by the absence of classes of interest. Surgical workflow and instrument recognition tasks are complicated in this manner, because of heavy data imbalances resulting from different lengths of phases and their erratic occurrences. Furthermore, the issue becomes difficult as sub-properties that help define phases, like instrument (co-)occurrence, are usually not considered when defining the split. We argue that such sub-properties must be equally considered. Methods: This work presents a publicly available data visualization tool that enables interactive exploration of dataset splits for surgical phase and instrument recognition. It focuses on the visualization of the occurrence of phases, phase transitions, instruments, and instrument combinations across sets. Particularly, it facilitates the assessment and identification of sub-optimal dataset splits. Results: We performed an analysis of common Cholec80 dataset splits using the proposed application and were able to uncover phase transitions and combinations of instruments that were not represented in one of the sets. Additionally, we outlined possible improvements to the splits. A user study with ten participants demonstrated the ability of participants to solve a selection of data exploration tasks using the proposed application. Conclusion: In highly unbalanced class distributions, special care should be taken with respect to the selection of an appropriate dataset split. Our interactive data visualization tool presents a promising approach for the assessment of dataset splits for surgical phase and instrument recognition. Evaluation results show that it can enhance the development of machine learning models. The application is available at https://cardio-ai.github.io/endovis-ml/ . |
| title | Surgical Phase and Instrument Recognition: How to identify appropriate Dataset Splits |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2306.16879 |