Abstract
In this work, we explore whether it is possible to learn representations of endoscopic video frames to perform tasks such as identifying surgical tool presence without supervision. We use a maximum mean discrepancy (MMD) variational autoencoder (VAE) to learn low-dimensional latent representations of endoscopic videos and manipulate these representations to distinguish frames containing tools from those without tools. We use three different methods to manipulate these latent representations in order to predict tool presence in each frame. Our fully unsupervised methods can identify whether endoscopic video frames contain tools with average precision of 71.56, 73.93, and 76.18, respectively, comparable to supervised methods. Our code is available at https://github.com/zdavidli/tool-presence/.
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Acknowledgements
This work was supported by the Johns Hopkins University Provost’s Postdoctoral fellowship, NVIDIA GPU grant, and other Johns Hopkins University internal funds. We would also like to thank Daniel Malinsky and Robert DiPietro for their invaluable feedback. We would also like to acknowledge the JHU Department of Computer Science providing a research GPU cluster.
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Li, D.Z., Ishii, M., Taylor, R.H., Hager, G.D., Sinha, A. (2020). Learning Representations of Endoscopic Videos to Detect Tool Presence Without Supervision. In: Syeda-Mahmood, T., et al. Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures. CLIP ML-CDS 2020 2020. Lecture Notes in Computer Science(), vol 12445. Springer, Cham. https://doi.org/10.1007/978-3-030-60946-7_6
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