Learning Representations of Endoscopic Videos to Detect Tool Presence Without Supervision
| Autor: | Masaru Ishii, Russell H. Taylor, David Z. Li, Ayushi Sinha, Gregory D. Hager |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Frame (networking) 02 engineering and technology Machine learning computer.software_genre Autoencoder 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering Code (cryptography) 020201 artificial intelligence & image processing Artificial intelligence business Feature learning computer |
| Zdroj: | Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures ISBN: 9783030609450 ML-CDS/CLIP@MICCAI |
| DOI: | 10.1007/978-3-030-60946-7_6 |
| Popis: | 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/. |
| Databáze: | OpenAIRE |
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