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Erschienen in:
05.05.2021 | Original Article
Against spatial–temporal discrepancy: contrastive learning-based network for surgical workflow recognition
Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 5/2021
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Purpose
Automatic workflow recognition from surgical videos is fundamental and significant for developing context-aware systems in modern operating rooms. Although many approaches have been proposed to tackle challenges in this complex task, there are still many problems such as the fine-grained characteristics and spatial–temporal discrepancies in surgical videos.
Methods
We propose a contrastive learning-based convolutional recurrent network with multi-level prediction to tackle these problems. Specifically, split-attention blocks are employed to extract spatial features. Through a mapping function in the step-phase branch, the current workflow can be predicted on two mutual-boosting levels. Furthermore, a contrastive branch is introduced to learn the spatial–temporal features that eliminate irrelevant changes in the environment.
Results
We evaluate our method on the Cataract-101 dataset. The results show that our method achieves an accuracy of 96.37% with only surgical step labels, which outperforms other state-of-the-art approaches.
Conclusion
The proposed convolutional recurrent network based on step-phase prediction and contrastive learning can leverage fine-grained characteristics and alleviate spatial–temporal discrepancies to improve the performance of surgical workflow recognition.