Unsupervised Learning for Surgical Motion by Learning to Predict the Future
| Autor: | Robert S. DiPietro, Gregory D. Hager |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry 0206 medical engineering Context (language use) 02 engineering and technology Conditional probability distribution Machine learning computer.software_genre 020601 biomedical engineering Motion (physics) 030218 nuclear medicine & medical imaging Task (project management) 03 medical and health sciences 0302 clinical medicine Mixture distribution Unsupervised learning Artificial intelligence F1 score business computer |
| Zdroj: | Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 ISBN: 9783030009366 MICCAI (4) |
| DOI: | 10.1007/978-3-030-00937-3_33 |
| Popis: | We show that it is possible to learn meaningful representations of surgical motion, without supervision, by learning to predict the future. An architecture that combines an RNN encoder-decoder and mixture density networks (MDNs) is developed to model the conditional distribution over future motion given past motion. We show that the learned encodings naturally cluster according to high-level activities, and we demonstrate the usefulness of these learned encodings in the context of information retrieval, where a database of surgical motion is searched for suturing activity using a motion-based query. Future prediction with MDNs is found to significantly outperform simpler baselines as well as the best previously-published result for this task, advancing state-of-the-art performance from an F1 score of \(0.60 \pm 0.14\) to \(0.77 \pm 0.05\). |
| Databáze: | OpenAIRE |
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